Classification of insurance policy holders into claimants and non-claimants

Naming Convention

A.xxxx where A is the case

xxxx.p =predict() =probability

xxxx.pred=prediction()

xxxx.perf=performance()

xx.tr=toggle output

There are two controls- 1. for selecting cases of handling missing data- select_case() 2. for selecting between balanced and imbalanced training data- toggle() —————————————————————- ROCR:performance(prediction,x) x is “ppv” for Precision. “tpr” for Recall. ————————————————-

setwd("D:/DA_INS/Main")
#save.image() # MUST run this to save data
set.seed(97) #MUST  run this before any other chunk
#if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggplot2,ROCR,ROSE, lightgbm,data.table,caret,MASS,dplyr,car,Matrix,MLmetrics,rcompanion,glmnet,keras,recipes)
#library(keras)
#install_keras()

ins=read.csv("InsuranceClaim.csv")
#recoding -1 to NA
ins[ins==-1]=NA
#removing ID
ins$id=NULL
#making cat/bin variables factor class
colnames(ins) # there are 57 potential predictor variables, 1 output, 1 ID
var_name=data.frame("name"=colnames(ins[,-1]))


#retrieving factor var names
library(dplyr)
  #nominal
  fac_var1=filter(var_name, grepl('cat|bin', var_name$name))
  #ordinal
  df=data.frame("Is_Int"=sapply(ins,is.integer))
  int_var=rownames(subset(df,df$Is_Int==TRUE))
  fac_var2=filter(var_name,!grepl('cat|bin', var_name$name) & var_name$name %in%  int_var)

# numeric var names
num_var=filter(var_name,!grepl('cat|bin', var_name$name) & !var_name$name %in% int_var)
#checking
57-(length(fac_var1$name)+length(fac_var2$name)+length(num_var$name))
#converting into factor
#nominal
ins[,fac_var1$name]=lapply(ins[,fac_var1$name],as.factor)
#ordinal

for (i in 1:length(fac_var2$name))
{
 ins[,fac_var2$name[i]]=factor( ins[,fac_var2$name[i]],ordered = TRUE, levels=min(na.omit(ins[,fac_var2$name[i]])):max(na.omit(ins[,fac_var2$name[i]])))
}
ins[,"target"]=as.factor(ins[,"target"])
str(ins)

#---standard R function cannot compute appropriate correlation for mixed variable type
mixed_assoc = function(df, cor_method="spearman", adjust_cramersv_bias=TRUE){
  df_comb = expand.grid(names(df), names(df),  stringsAsFactors = F) %>% set_names("X1", "X2")
  
  is_nominal = function(x) class(x) %in% c("factor", "character")
  
  is_numeric <- function(x) { is.integer(x) || is_double(x)}
  
  f = function(xName,yName) {
    x =  pull(df, xName)
    y =  pull(df, yName)
    
    result = if(is_nominal(x) && is_nominal(y)){
      cv = cramerV(as.character(x), as.character(y), bias.correct =TRUE)
      data.frame(xName, yName, assoc=cv, type="cramersV")
      
    }else if(is_numeric(x) && is_numeric(y)){
      correlation = cor(x, y, method=cor_method, use="complete.obs")
      data.frame(xName, yName, assoc=correlation, type="correlation")
      
    }else if(is_numeric(x) && is_nominal(y)){
      r_squared = summary(lm(x ~ y))$r.squared
      data.frame(xName, yName, assoc=sqrt(r_squared), type="anova")
      
    }else if(is_nominal(x) && is_numeric(y)){
      r_squared = summary(lm(y ~x))$r.squared
      data.frame(xName, yName, assoc=sqrt(r_squared), type="anova")
      
    }else {
      warning(paste("unmatched column type combination: ", class(x), class(y)))
    }
    
    # finally add complete obs number and ratio to table
    result %>% mutate(complete_obs_pairs=sum(!is.na(x) & !is.na(y)), complete_obs_ratio=complete_obs_pairs/length(x)) %>% rename(x=xName, y=yName)
  }
  
  # apply function to each variable combination
  map2_df(df_comb$X1, df_comb$X2, f)
}
#----------for instances where ordered factors are problematic----
ord_to_fac=function(x)
{
  copy=data.frame(x)
  copy[,names(copy) %in% fac_var2$name]=lapply(copy[,names(copy) %in% fac_var2$name],function(x) as.factor(as.character(x)))
result=copy
}

#-----------back_to_ord-------------------

back_to_ord=function(x)
{
temp=names(x[,names(x) %in% fac_var2$name])
for (i in 1:length(temp))
{
 x[,temp[i]]=factor( x[,temp[i]],ordered = TRUE, levels=min(as.numeric(as.character(x[,temp[i]]))):max(as.numeric(as.character(x[,temp[i]]))))
}
return(x)
}

cat("Claim cases are", 100*(nrow(subset(ins,target==0))/nrow(ins)),"per cent")

Claim cases are 96.35525 per cent

cat("Non-claim cases are", 100*(nrow(subset(ins,target==1))/nrow(ins))," per cent")

Non-claim cases are 3.644752  per cent

“calc” features are suspected weakest predictors. It is decided that they are removed before any modelling.

#names of calc features

calc_var=filter(var_name, grepl('calc', var_name$name))

#Start here

case_select=function(x)
{
  if(x=="A")
  { return(ins_A)}
  if(x=="B")
  { return(ins_B)}
  if(x=="C")
  { return(ins_C)}
}
data=case_select("A") #A or B or C

#consider removing predictors that have zero variance
library(caret)
#nearZeroVar(data[,-1],saveMetrics = TRUE)

#partitioning
rec=createDataPartition(data$target,p=2/3,list=F)
train=data[rec,]
val=data[-rec,]

cat("Train data has",table(train$target)[2]*100/sum(table(train$target))," % class-1","\n")

Train data has 4.538469  % class-1 

cat("Train data has",table(train$target)[1]*100/sum(table(train$target))," % class-0")

Train data has 95.46153  % class-0

library(ROSE) # ordered factors are not accepted

train.bal=ord_to_fac(train) 
train.bal=ROSE(target~ ., data =train.bal, seed = 97)$data
train.bal=back_to_ord(train.bal)
cat("Balanced train data has",100*table(train.bal$target)[2]/sum(table(train.bal$target)),"% class-1 cases")

Balanced train data has 49.8091 % class-1 cases

table(train.bal$target)

    0     1 
41803 41485 

#Switch between balanced and unbalanced train data
#1 for balance,0 for unbalance
p=0
toggle=function(x)
{
  if(x==1)
  { cat("balanced data in use")
  invisible(train.bal) 
  }
  else {
    cat("unbalanced data in use")
  invisible(train)
  }
}

tr=toggle(p)

unbalanced data in use

table(tr$target)

    0     1 
79508  3780 

ROSE output is used only for logreg and en. For other models,p can take only 0

train.mat=as.matrix(ord_to_fac(tr))
mode(train.mat)="numeric"


#find linearly dependent columns-----------------
rankifremoved <- sapply(1:ncol(train.mat), function (x) qr(train.mat[,-x])$rank)
ld.ind=which(rankifremoved == max(rankifremoved))
ld.ind

[1] 11 12 13 14 15

ld.names=colnames(train.mat[,ld.ind])
ld.names

[1] "ps_ind_10_bin" "ps_ind_11_bin" "ps_ind_12_bin" "ps_ind_13_bin"
[5] "ps_ind_14"    

#pca-----
pca=prcomp(train.mat[,-1],scale=TRUE)
#finding no. of PC------------
pca.imp=summary(pca)$importance
plot(pca.imp[2,],ylab="Prop of Var",xlab="PC")

plot(pca.imp[3,],ylab="Cum Prop of Var",xlab="PC")

train.pca=data.frame("target"=train.mat[,1],pca$x)
train.pca=train.pca[,1:34] # 33 PC+target
table(train.pca$target)

    0     1 
79508  3780 

#saveRDS(pca$rotation,"B_unb_pca.rds")

#PC for val data
val.mat=as.matrix(ord_to_fac(val))
mode(val.mat)="numeric"
val.pca=data.frame(predict(pca,newdata=val.mat[,-1]))
val.pca=data.frame("target"=val$target,val.pca[,1:33]) 

LogLoss(predict(log1,train.pca,type="response"),train.mat[,1])

[1] 0.1808006

LogLoss(log1.p,val.mat[,1])

[1] 0.181084

LogLoss(predict(glm2,tr[,!names(tr) %in% outlist],type="prob")[,2],as.numeric(as.character(tr$target)))

[1] 0.1837023

LogLoss(glm2.p[,2],as.numeric(as.character(val$target)))

[1] 0.1837871

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls

library(lightgbm)

#---------------------LGB1-Using PC#--------------------------
toggle(p)

unbalanced data in use

table(train.pca$target) # second-check

     0      1 
284738  10950 

#ensure target is numeric,gives num whatever be the type of input
train.pca$target=as.numeric(as.character(train.pca$target))

val.pca$target=as.numeric(as.character(val.pca$target))

#prepare training and validation data
lgb1.train=sparse.model.matrix(target~., data =train.pca)
lgb1.val =sparse.model.matrix(target~., data=val.pca)

lgb1.train_mat = lgb.Dataset(data = as.matrix(lgb1.train), label =train.pca$target)
lgb1.val_mat= lgb.Dataset(data = as.matrix(lgb1.val), label =val.pca$target)

valid1 = list(test =lgb1.val_mat)

#lgb1.col=lgb1.train_mat$get_colnames()
#lgb1.col

# model parameters
lgb1.gridS =expand.grid(min_sum_hessian_in_leaf =c(0.05,0.5,1),
                          feature_fraction =c(0.6,0.7,0.8), 
                          bagging_fraction =c(0.6,0.7,0.8), 
                          bagging_freq =c(2,4), 
                          lambda_l1 =c(0.2,0.4,1), 
                          lambda_l2 = c(0.2,0.4,1), 
                          min_data_in_bin=100,
                          min_gain_to_split = c(0.5,1,2), 
                          min_data_in_leaf =c(1000,1500)
                         )
perf1=numeric(nrow(lgb1.gridS))

for(i in 1:nrow(lgb1.gridS))
{        
        lgb1 =lightgbm(params = list(objective = "binary",
                      metric="binary_logloss",
                      min_sum_hessian_in_leaf=lgb1.gridS[i,"min_sum_hessian_in_leaf"],
                      feature_fraction =lgb1.gridS[i,"feature_fraction"], 
                      bagging_fraction =lgb1.gridS[i,"bagging_fraction"], 
                      bagging_freq =lgb1.gridS[i,"bagging_freq"], 
                      lambda_l1 =lgb1.gridS[i,"lambda_l1"], 
                      lambda_l2 = lgb1.gridS[i,"lambda_l2"],
                      min_data_in_bin=lgb1.gridS[i,"min_data_in_bin"],
                      min_gain_to_split =lgb1.gridS[i,"min_gain_to_split"], 
                      min_data_in_leaf = lgb1.gridS[i,"min_data_in_leaf"],
                      is_unbalance=as.logical(1-p)),
                      data=lgb1.train_mat,
                      learning_rate=0.02,
                      num_leaves = 15,
                      valids=valid1, 
                      nrounds =2) #categorical features are to be declared inside IFF the input data is not properly tagged
        cat("running iteration:",i)
perf1[i]=min(rbindlist(lgb1$record_evals$test$binary_logloss))
gc(verbose=FALSE)
}

[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 1[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 2[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 3[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 4[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 5[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 6[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 7[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 8[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 9[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 10[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 11[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 12[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 13[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 14[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 15[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 16[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 17[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 18[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 19[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 20[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 21[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 22[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 23[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 24[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 25[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009627 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 26[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 27[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 28[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 29[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 30[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 31[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 32[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 33[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 34[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 35[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032958 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 36[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 37[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 38[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 39[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 40[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 41[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 42[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 43[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 44[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 45[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 46[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 47[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 48[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 49[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 50[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 51[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 52[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 53[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 54[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 55[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 56[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 57[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073762 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 58[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 59[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 60[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 61[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 62[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 63[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 64[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 65[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 66[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 67[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 68[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 69[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033816 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 70[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 71[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 72[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 73[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 74[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033885 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 75[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 76[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 77[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 78[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 79[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 80[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 81[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 82[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032987 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 83[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 84[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 85[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 86[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 87[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008839 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 88[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 89[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 90[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 91[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 92[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 93[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 94[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 95[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 96[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 97[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 98[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 99[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034038 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070969 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033811 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035032 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069806 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008481 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032615 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032963 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033773 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034036 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033982 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033817 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032905 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033815 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033134 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033049 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033601 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032678 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032885 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033038 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032953 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037134 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033762 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033886 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031940 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032691 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033984 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032601 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032879 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074966 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032808 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031813 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076783 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079729 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071882 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034716 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032936 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032993 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033983 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035958 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032749 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032866 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032164 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033003 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033035 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070959 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032762 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071746 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031815 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074003 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032995 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033058 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072088 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032056 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075035 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033931 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071766 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031969 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033075 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032993 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073006 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033593 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072882 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033049 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033831 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031980 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031987 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073083 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033046 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032027 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033783 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033678 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032954 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071678 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032036 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033907 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034010 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031960 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031847 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033976 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032756 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031996 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033016 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032993 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033811 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073940 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031998 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031995 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 916[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 917[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 918[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035123 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 919[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 920[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031958 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 921[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 922[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 923[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 924[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 925[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071838 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 926[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 927[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 928[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 929[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 930[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 931[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 932[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 933[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 934[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 935[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 936[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 937[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 938[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 939[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 940[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 941[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 942[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 943[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 944[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 945[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 946[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 947[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 948[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 949[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 950[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033953 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 951[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 952[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 953[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 954[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 955[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 956[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 957[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 958[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 959[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 960[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 961[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 962[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 963[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 964[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 965[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 966[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 967[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 968[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 969[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 970[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 971[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 972[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 973[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 974[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 975[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 976[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 977[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 978[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 979[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 980[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 981[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 982[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 983[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 984[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 985[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 986[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 987[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 988[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 989[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 990[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 991[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 992[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 993[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 994[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 995[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 996[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 997[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033729 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 998[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 999[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 1000[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 1001[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.16178 
running iteration: 1002[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1003[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031965 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1004[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1005[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1006[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1007[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1008[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1009[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1010[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1011[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 1012[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 1013[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161785 
running iteration: 1014[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1015[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1016[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1017[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 1018[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 1019[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161804 
running iteration: 1020[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1021[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1022[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1023[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1024[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1025[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1026[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1027[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1028[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1029[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1030[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1031[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1032[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1033[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1034[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1035[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1036[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1037[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1038[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1039[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008411 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1040[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1041[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1042[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1043[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033959 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1044[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1045[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1046[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1047[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1048[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1049[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1050[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1051[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1052[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1053[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1054[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1055[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1056[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032820 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1057[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1058[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1059[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1060[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1061[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161744 
running iteration: 1062[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1063[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1064[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070840 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1065[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1066[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1067[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161784 
running iteration: 1068[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1069[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1070[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032980 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161768 
running iteration: 1071[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1072[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1073[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1074[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1075[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1076[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1077[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1078[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1079[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1080[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1081[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1082[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1083[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1084[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1085[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1086[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1087[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1088[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034736 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1089[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1090[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1091[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1092[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1093[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1094[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1095[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1096[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1097[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1098[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1099[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033910 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032951 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032972 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161779 
running iteration: 1110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033051 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.16161  test's binary_logloss:0.161743 
running iteration: 1116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.159091 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161801 
running iteration: 1119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161784 
running iteration: 1122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161767 
running iteration: 1125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032123 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161804 
running iteration: 1128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073006 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073051 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033034 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034059 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070961 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032746 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030978 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161609 test's binary_logloss:0.161742 
running iteration: 1224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161783 
running iteration: 1230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161803 
running iteration: 1236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032791 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033840 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070935 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161778 
running iteration: 1272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161742 
running iteration: 1278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15903  test's binary_logloss:0.15909 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.1618 
running iteration: 1281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161783 
running iteration: 1284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158995 test's binary_logloss:0.159052 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161766 
running iteration: 1287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161805 
running iteration: 1290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033051 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073043 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071984 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031931 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161798 
running iteration: 1335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161803 
running iteration: 1344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034743 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032685 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034978 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033837 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033033 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033158 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161605 test's binary_logloss:0.161738 
running iteration: 1386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161798 
running iteration: 1389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031939 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161774 
running iteration: 1392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035036 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032987 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032886 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161604 test's binary_logloss:0.161737 
running iteration: 1440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159033 test's binary_logloss:0.159086 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161797 
running iteration: 1443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159021 test's binary_logloss:0.159082 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.161774 
running iteration: 1446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032791 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161751 
running iteration: 1449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161802 
running iteration: 1452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033022 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032833 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069923 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031882 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 1545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032955 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072615 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033983 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 1596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071906 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 1605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035058 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 1611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033691 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032969 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 1620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070813 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032006 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033736 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036022 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 1650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 1653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 1656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 1668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034075 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035980 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072933 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031984 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 1713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 1719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 1722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 1725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 1728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032879 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032785 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033749 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032885 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 1758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 1761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034028 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 1764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 1767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 1770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032972 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 1773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032591 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 1776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033931 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 1779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 1782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 1818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 1830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032740 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 1833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033887 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034027 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.019693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097806 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008291 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069999 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032973 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 1869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 1875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032953 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032716 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.156789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.150706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.140024 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.145253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100969 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1916[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1917[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1918[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1919[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 1920[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.121771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1921[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.056933 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1922[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 1923[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1924[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1925[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.126105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 1926[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1927[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1928[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 1929[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1930[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116996 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1931[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 1932[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082651 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1933[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1934[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 1935[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1936[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1937[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 1938[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1939[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1940[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 1941[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1942[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1943[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 1944[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1945[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1946[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1947[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1948[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1949[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046010 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1950[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034001 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1951[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1952[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1953[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1954[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1955[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1956[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1957[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1958[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1959[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1960[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1961[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1962[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1963[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1964[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1965[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.067937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1966[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040088 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1967[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1968[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1969[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1970[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.018140 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1971[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1972[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1973[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1974[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1975[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1976[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 1977[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1978[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1979[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.112090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 1980[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1981[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073559 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1982[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097998 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 1983[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1984[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1985[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 1986[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1987[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1988[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 1989[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1990[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1991[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 1992[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1993[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1994[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088827 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 1995[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1996[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1997[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 1998[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 1999[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2000[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2001[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2002[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2003[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2004[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2005[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2006[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2007[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2008[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2009[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2010[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2011[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2012[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2013[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2014[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2015[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082993 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2016[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.122128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2017[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2018[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2019[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2020[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2021[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2022[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2023[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2024[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2025[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2026[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2027[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2028[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.127063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2029[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2030[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2031[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2032[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099978 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2033[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2034[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2035[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2036[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2037[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2038[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2039[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2040[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2041[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.129679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2042[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2043[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2044[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2045[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2046[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2047[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2048[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2049[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104965 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2050[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2051[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2052[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2053[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2054[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2055[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2056[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2057[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2058[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2059[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2060[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2061[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084838 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2062[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2063[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2064[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2065[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2066[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2067[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099683 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2068[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2069[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2070[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2071[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2072[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2073[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2074[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2075[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2076[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2077[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.019922 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2078[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095787 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2079[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2080[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2081[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2082[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2083[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2084[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2085[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2086[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2087[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2088[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2089[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2090[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2091[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2092[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2093[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046045 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2094[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2095[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2096[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.112084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2097[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2098[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2099[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046936 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092820 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.147608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.018916 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.125467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087823 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095831 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037960 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084954 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042833 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039791 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083943 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.113108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076966 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008415 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085761 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.150108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087003 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008612 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084887 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.114685 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088021 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089158 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088743 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008451 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035806 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034035 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036772 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076939 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076797 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033951 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034158 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034740 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036877 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034525 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079034 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033959 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083791 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034021 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033965 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038058 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033756 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035907 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009789 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081817 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032837 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033947 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100813 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033954 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032915 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036710 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034710 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036863 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.16176 
running iteration: 2463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159089 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034811 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034792 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035035 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161782 
running iteration: 2514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035034 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.16176 
running iteration: 2517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161613 test's binary_logloss:0.161743 
running iteration: 2520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161805 
running iteration: 2523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158996 test's binary_logloss:0.159045 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161757 
running iteration: 2529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159038 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161664 test's binary_logloss:0.161812 
running iteration: 2535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035830 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161775 
running iteration: 2538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032987 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033920 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037969 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161781 
running iteration: 2568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068785 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161805 
running iteration: 2577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035887 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159023 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161775 
running iteration: 2580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161651 test's binary_logloss:0.161756 
running iteration: 2583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.1618 
running iteration: 2586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159016 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161775 
running iteration: 2592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033788 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032788 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034046 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009034 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.074881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033860 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.142941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161781 
running iteration: 2622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.126063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.161759 
running iteration: 2625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.067271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161612 test's binary_logloss:0.161742 
running iteration: 2628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.079026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045936 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161684 test's binary_logloss:0.161799 
running iteration: 2640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085164 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128970 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009127 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043016 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044030 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159075 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161804 
running iteration: 2685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161755 
running iteration: 2691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161799 
running iteration: 2694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161663 test's binary_logloss:0.161811 
running iteration: 2697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.059668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158994 test's binary_logloss:0.159054 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161774 
running iteration: 2700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.059304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.082023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099876 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.16178 
running iteration: 2730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041773 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159012 test's binary_logloss:0.159074 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161758 
running iteration: 2733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088924 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094854 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158984 test's binary_logloss:0.159043 
[2]:    train's binary_logloss:0.161611 test's binary_logloss:0.161741 
running iteration: 2736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159031 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161804 
running iteration: 2739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096993 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.084794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.159081 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161774 
running iteration: 2742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.15904 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161747 
running iteration: 2745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159097 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161803 
running iteration: 2748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159088 
[2]:    train's binary_logloss:0.161662 test's binary_logloss:0.16181 
running iteration: 2751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161774 
running iteration: 2754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043651 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100651 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036979 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.093307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087559 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096036 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090979 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046693 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103940 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161738 
running iteration: 2790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.059445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161801 
running iteration: 2802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161776 
running iteration: 2805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.142819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085028 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.113382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.112337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089816 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037057 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.088350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.024807 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.118755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.200653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.139517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159011 test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161754 
running iteration: 2841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.141871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.136950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161798 
running iteration: 2847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090030 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.085194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.092065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089158 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.097956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.102148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087032 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.086114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.130458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.089652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.146139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.127147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.121345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111860 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.020619 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.235822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.209699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.120606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159025 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161776 
running iteration: 2892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.160151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.15901  test's binary_logloss:0.159073 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161753 
running iteration: 2895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.123241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.148146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158983 test's binary_logloss:0.159041 
[2]:    train's binary_logloss:0.161608 test's binary_logloss:0.161737 
running iteration: 2898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.161835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.117201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159034 test's binary_logloss:0.159078 
[2]:    train's binary_logloss:0.161678 test's binary_logloss:0.161797 
running iteration: 2901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106827 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159022 test's binary_logloss:0.15908 
[2]:    train's binary_logloss:0.161665 test's binary_logloss:0.161771 
running iteration: 2904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158997 test's binary_logloss:0.159046 
[2]:    train's binary_logloss:0.161643 test's binary_logloss:0.161754 
running iteration: 2907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104050 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159037 test's binary_logloss:0.159096 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.1618 
running iteration: 2910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.162338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.117715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159015 test's binary_logloss:0.159083 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161776 
running iteration: 2913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.172747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.094503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.153087 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.158993 test's binary_logloss:0.159053 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.16177 
running iteration: 2916

#optimal parameters
min(perf1)

[1] 0.1590396

lgb1.gridS[which.min(perf1),]

cat("Choose Model",which.min(perf1))

Choose Model 1772

k1=which.min(perf2)

lgb1.grid=list(objective = "binary",
                metric="binary_logloss",
                min_sum_hessian_in_leaf=lgb2.gridS[k1,"min_sum_hessian_in_leaf"],
                feature_fraction =lgb2.gridS[k1,"feature_fraction"], 
                bagging_fraction =lgb2.gridS[k1,"bagging_fraction"], 
                bagging_freq =lgb2.gridS[k1,"bagging_freq"], 
                lambda_l1 =lgb2.gridS[k1,"lambda_l1"], 
                lambda_l2 = lgb2.gridS[k1,"lambda_l2"], 
                min_data_in_bin=lgb2.gridS[k1,"min_data_in_bin"],
                min_gain_to_split =lgb2.gridS[k1,"min_gain_to_split"], 
                min_data_in_leaf = lgb2.gridS[k1,"min_data_in_leaf"],
                is_unbalance=as.logical(1-p))

lgb1 =lightgbm(params =lgb1.grid ,
                data=lgb1.train_mat,
                learning_rate=0.02,
                early_stopping_rounds=10, 
                num_leaves = 15,
                valids=valid1, 
                nrounds =lgb1[["best_iter"]])

[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 8415
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 33
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159026 test's binary_logloss:0.159079 

saveRDS.lgb.Booster(lgb1,"unb_pca_lgb.rds")


#VarImp-------------

lgb1.imp=lgb.importance(lgb1,percentage = TRUE)
lgb.plot.importance(lgb1.imp)

# LGB1 evaluation --------------

lgb1.p=predict(lgb1,lgb1.val)
lgb1.pred=prediction(lgb1.p,as.factor(val.pca$target))
lgb1.perf=performance(lgb1.pred,"f")
plot(lgb1.perf) #colorize=T
abline(a=0,b=1)

LogLoss(lgb1.p,val.pca$target)

[1] 0.1590794

#--------------------#LGB2-Using Original Predictors----

tr$target=as.numeric(as.character(tr$target))
val$target=as.numeric(as.character(val$target))

#Construct training and validation data


lgb2.train= sparse.model.matrix(target~., data =tr[,!names(tr) %in% outlist])
lgb2.val = sparse.model.matrix(target~., data=val[,!names(val) %in% outlist])

lgb2.train_mat= lgb.Dataset(data = as.matrix(lgb2.train), label =tr$target,free_raw_data = FALSE)
lgb2.val_mat = lgb.Dataset(data = as.matrix(lgb2.val), label =val$target)

valid2 = list(test =lgb2.val_mat)
lgb2.col=lgb2.train_mat$get_colnames()


#expand.grid to build grid search- all possible combinations of input values

lgb2.gridS =expand.grid(min_sum_hessian_in_leaf =c(0.05,0.5,1),
                          feature_fraction =c(0.6,0.7,0.8), 
                          bagging_fraction =c(0.6,0.7,0.8), 
                          bagging_freq =c(2,4), 
                          lambda_l1 =c(0.2,0.4,1), 
                          lambda_l2 = c(0.2,0.4,1), 
                          min_data_in_bin=100,
                          min_gain_to_split = c(0.5,1,2), 
                          min_data_in_leaf =c(1000,1500)
                         )

perf2=numeric(nrow(lgb2.gridS)) # empty numeric,same row num as gridS



# for choosing number of iterations
#A.lgb2.cv=lgb.cv(params = A.lgb2.grid,data=A.lgb2.train_mat,learning_rate=0.02,num_leaves = 20, #nrounds = 1000,eval_freq = 20, eval = "binary_logloss",nfold = 5, stratified = TRUE )

for(i in 1:nrow(lgb2.gridS))
{        
        lgb2 =lightgbm(params = list(objective = "binary",
                metric="binary_logloss",
                min_sum_hessian_in_leaf=lgb2.gridS[i,"min_sum_hessian_in_leaf"],
                feature_fraction =lgb2.gridS[i,"feature_fraction"], 
                bagging_fraction =lgb2.gridS[i,"bagging_fraction"], 
                bagging_freq =lgb2.gridS[i,"bagging_freq"], 
                lambda_l1 =lgb2.gridS[i,"lambda_l1"], 
                lambda_l2 = lgb2.gridS[i,"lambda_l2"],
                min_data_in_bin=lgb2.gridS[i,"min_data_in_bin"],
                min_gain_to_split =lgb2.gridS[i,"min_gain_to_split"], 
                min_data_in_leaf = lgb2.gridS[i,"min_data_in_leaf"],
                is_unbalance=as.logical(1-p)),
                data=lgb2.train_mat,
                learning_rate=0.02,
                num_leaves = 15,
                valids=valid2, 
                nrounds =2) #categorical features are to be declared inside IFF the input data is not properly tagged
        cat("running iteration:",i)
perf2[i]=min(rbindlist(lgb2$record_evals$test$binary_logloss))
gc(verbose=FALSE)
}

[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078438 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 1[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.391947 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 2[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035010 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 3[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077381 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 4[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079918 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 5[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.119084 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 6[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 7[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.126893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 8[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 9[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 10[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084182 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 11[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 12[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075055 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 13[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099633 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 14[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078261 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 15[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091127 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 16[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082913 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 17[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082007 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 18[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 19[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088682 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 20[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260080 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 21[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084302 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 22[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.124055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 23[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.041691 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 24[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 25[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084869 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 26[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080886 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 27[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075294 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 28[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 29[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.312047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 30[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 31[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076639 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 32[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031905 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 33[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074411 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 34[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082669 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 35[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073078 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 36[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088549 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 37[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.416488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 38[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.112055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 39[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.333250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 40[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 41[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262126 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 42[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081412 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 43[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082464 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 44[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077373 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 45[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076912 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 46[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.055185 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 47[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038321 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 48[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 49[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075481 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 50[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079913 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 51[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085879 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 52[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075859 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 53[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086557 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 54[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 55[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077797 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 56[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030812 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 57[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 58[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073656 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 59[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067972 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 60[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083378 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 61[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 62[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.427827 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 63[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037034 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 64[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058038 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 65[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084217 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 66[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072564 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 67[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090404 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 68[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086252 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 69[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079427 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 70[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 71[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 72[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082004 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 73[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 74[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076190 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 75[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078966 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 76[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082307 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 77[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 78[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085011 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 79[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090093 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 80[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076781 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 81[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079169 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 82[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075031 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 83[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.055767 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 84[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 85[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031602 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 86[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 87[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080805 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 88[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 89[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 90[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079932 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 91[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272915 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 92[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275932 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 93[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 94[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086971 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 95[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 96[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034035 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 97[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084290 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 98[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082372 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 99[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099293 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069803 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076602 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.334479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035614 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.120124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079994 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081800 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072071 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.286664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090302 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082117 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.204584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076526 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.102245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076534 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092183 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096248 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093780 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.454129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271966 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081845 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089352 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034444 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.123587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091115 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077739 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077050 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068129 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086978 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076963 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078123 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083931 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082829 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.328317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076698 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034658 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028400 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084034 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093161 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081837 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082618 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072955 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087181 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086191 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.114782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056157 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076127 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.289453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091500 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076770 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075625 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084747 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079045 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081713 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081185 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029829 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.363944 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087879 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.277320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073254 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.283625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078807 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090500 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092943 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081427 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.439626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.053380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083148 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094741 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087259 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077605 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.107510 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082661 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064230 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035208 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.153967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083157 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089602 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.350798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096837 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087816 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083556 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082396 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080593 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080902 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077675 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084951 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098404 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076124 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081661 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071397 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078737 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039765 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.299705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072084 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038099 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076240 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070769 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084054 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080384 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081378 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083943 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034185 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081333 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080453 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056359 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031892 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033358 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071297 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078356 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.360566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088491 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.100891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089547 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058624 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035556 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090902 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081529 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078920 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072754 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079136 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085909 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086681 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078358 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082454 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082248 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074674 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029667 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076160 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075139 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096516 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086946 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105088 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.113073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074001 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078348 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081173 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088618 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089875 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091982 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076555 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082286 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080479 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081414 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039352 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.121040 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.334720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088444 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083951 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.127063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.114682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.370303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.343694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086927 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279910 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080842 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081433 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073269 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039247 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084107 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079589 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078771 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087648 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078240 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072784 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079146 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.113728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034500 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088488 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074525 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080966 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074346 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076213 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077049 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089930 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081542 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089201 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266808 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082184 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078854 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033686 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084562 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.104188 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077097 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.326292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088129 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094926 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090442 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084726 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082798 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255941 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083033 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077662 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093924 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093621 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.383671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268884 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072798 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.102003 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092161 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036785 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087043 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.108694 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077627 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085254 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073764 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.052714 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039773 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079612 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086228 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078649 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081626 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083830 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080940 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034849 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.100789 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081174 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078214 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.290241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074861 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081002 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081775 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080362 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082980 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079183 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034090 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033864 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032668 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088658 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080581 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092972 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079308 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075893 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086518 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033324 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.043911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087944 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081803 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086585 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072285 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076861 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088144 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.108763 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087238 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080827 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.096522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.130545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092125 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075323 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086050 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028776 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071456 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075636 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.232842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080058 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088501 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074793 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030655 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.317383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071747 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074424 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029989 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032774 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073746 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079283 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071849 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.147167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070879 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073448 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.234917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071778 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027768 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030976 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075020 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070486 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.239965 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265634 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028060 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031402 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079578 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.055962 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031849 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071917 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082438 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072329 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026117 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027470 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079656 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075412 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079735 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076451 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.181975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027266 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.234295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065307 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077043 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028096 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083390 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.236104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078053 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.345721 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087373 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084790 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.124647 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089384 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084314 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073432 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083021 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083530 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.321112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075838 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085388 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094311 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080568 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031790 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.162023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081890 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072431 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073233 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088954 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091002 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.287184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077415 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080842 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033682 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090386 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073718 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079843 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075252 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033701 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074710 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083874 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081549 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084055 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085279 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091646 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077948 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082270 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.327277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033845 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082258 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088428 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087292 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082674 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076332 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077106 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079152 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088777 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087018 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070774 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072597 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086621 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074403 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081757 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088816 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081852 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.338518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077358 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073040 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087433 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075667 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086936 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079128 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080214 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083926 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078726 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076476 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080711 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076842 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087150 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083827 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.145687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092551 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072542 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085565 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.117497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097205 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082379 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080089 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079962 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059892 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037395 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031532 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082979 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082365 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073211 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085575 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079222 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275970 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071749 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083600 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030902 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067341 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083008 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.287652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.270017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088700 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.352454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.050955 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036444 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.240462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098166 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083963 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074397 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265943 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061984 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031623 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093312 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082482 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071581 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.274085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080805 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097856 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074687 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077552 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077121 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.315951 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.127373 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.369848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096735 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075773 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077695 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078542 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084087 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085253 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092590 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037596 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035391 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253016 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075514 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083538 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090553 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081346 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083626 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.151028 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082276 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083823 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089426 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.387873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091694 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076223 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083511 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273951 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077803 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078934 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035989 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084893 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085835 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084900 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080823 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079045 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081257 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059123 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036774 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085003 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081124 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080415 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263914 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079991 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075859 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083453 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079033 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077666 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.287526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039652 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.049155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095512 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076712 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073537 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074483 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082947 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081524 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077145 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088762 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.060899 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028460 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.111332 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068481 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081848 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084073 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083676 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031887 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078889 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092059 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075860 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077687 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.334771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083567 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089972 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.322528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077307 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081834 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088766 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085284 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080669 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088117 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109816 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086059 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073554 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081792 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084235 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086391 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080473 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085018 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076722 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040049 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081450 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.280204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070889 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064162 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092330 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085577 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084566 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082682 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083349 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078025 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073674 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082001 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.121557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.290617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095957 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083281 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081502 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080295 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078729 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038946 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081762 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075990 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085342 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088399 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073403 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086957 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036274 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080018 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073806 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.101368 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077969 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083491 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087009 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084303 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077343 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092120 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078906 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082323 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090360 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063876 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034909 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031589 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.270408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084076 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073357 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077154 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070778 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076852 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093905 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073848 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094684 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090370 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087760 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086785 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081391 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034311 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081189 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076761 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.102468 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092000 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084991 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073778 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066775 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.436771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087236 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087950 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087390 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088886 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036185 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074165 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081448 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081090 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082855 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.229876 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096149 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075409 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085975 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093460 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.285866 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081907 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075831 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033620 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076654 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092488 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070784 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076766 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090041 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066456 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037703 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034094 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084262 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073856 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098944 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076720 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.103914 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089751 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083252 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082552 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088091 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 916[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082140 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 917[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 918[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091228 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 919[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074583 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 920[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096106 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 921[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074707 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 922[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032752 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 923[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087052 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 924[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 925[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 926[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 927[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084876 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 928[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 929[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081132 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 930[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 931[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079357 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 932[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 933[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086770 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 934[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079178 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 935[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033862 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 936[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084781 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 937[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 938[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077668 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 939[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 940[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 941[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081914 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 942[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077311 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 943[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 944[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.111021 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 945[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071093 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 946[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035601 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 947[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 948[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093215 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 949[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.100330 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 950[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080633 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 951[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.114901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 952[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 953[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089953 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 954[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088303 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 955[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068530 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 956[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 957[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083305 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 958[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 959[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088528 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 960[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085671 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 961[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081125 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 962[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083341 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 963[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081235 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 964[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077286 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 965[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 966[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079233 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 967[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075886 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 968[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075526 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 969[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 970[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073319 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 971[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080859 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 972[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 973[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 974[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084443 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 975[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 976[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038173 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 977[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081715 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 978[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 979[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087486 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 980[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 981[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077755 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 982[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.123140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 983[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080065 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 984[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106680 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 985[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106177 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 986[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079138 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 987[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082749 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 988[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089778 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 989[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 990[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036968 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 991[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.297547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 992[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 993[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 994[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090859 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 995[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082069 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 996[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092470 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 997[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 998[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089242 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 999[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 1000[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 1001[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.285332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161697 
running iteration: 1002[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073579 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 1003[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082279 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 1004[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085758 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161713 
running iteration: 1005[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077139 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 1006[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033476 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 1007[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077318 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161685 
running iteration: 1008[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 1009[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 1010[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.334512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161745 
running iteration: 1011[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087936 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1012[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.119869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1013[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040386 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1014[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090207 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1015[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099389 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1016[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1017[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1018[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078697 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1019[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095664 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1020[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035878 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1021[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.127493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1022[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1023[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1024[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080990 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1025[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.297130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1026[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073955 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1027[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.288966 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1028[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073089 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1029[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083184 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1030[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036816 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1031[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083980 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1032[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1033[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080579 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1034[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1035[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266970 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1036[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.162076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1037[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1038[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076723 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1039[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1040[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077505 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1041[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1042[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079490 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1043[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081577 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1044[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087293 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1045[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073792 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1046[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1047[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087483 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1048[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077008 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1049[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.330171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1050[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077309 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1051[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095860 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1052[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031156 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1053[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1054[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077086 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1055[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1056[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.123419 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1057[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084028 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1058[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.399747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1059[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1060[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1061[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080527 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161632 test's binary_logloss:0.161684 
running iteration: 1062[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081552 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1063[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091842 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1064[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.274469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1065[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078601 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1066[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084646 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1067[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076887 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161744 
running iteration: 1068[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1069[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1070[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.045276 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161723 
running iteration: 1071[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032538 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1072[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.330552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1073[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.336622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1074[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088895 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1075[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1076[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1077[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080341 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1078[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1079[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083749 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1080[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081677 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1081[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.290220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1082[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035757 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1083[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.119526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1084[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1085[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268787 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1086[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098595 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1087[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083151 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1088[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1089[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1090[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1091[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1092[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082277 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1093[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.278157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1094[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076292 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1095[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081521 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1096[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1097[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085664 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1098[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.334521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1099[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075224 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077408 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037400 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088073 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099434 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085320 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.228861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161654 test's binary_logloss:0.161697 
running iteration: 1110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075764 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161712 
running iteration: 1113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076256 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093268 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085408 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 
[2]:    train's binary_logloss:0.161631 test's binary_logloss:0.161684 
running iteration: 1116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072486 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161676 test's binary_logloss:0.161745 
running iteration: 1119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079550 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.283446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161674 test's binary_logloss:0.161743 
running iteration: 1122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078353 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074736 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.308898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161722 
running iteration: 1125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093572 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085720 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070701 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077078 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067757 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087630 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085594 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084318 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080580 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.293968 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069212 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.320008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085343 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073470 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.277206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.101645 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079523 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083913 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039705 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081086 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073797 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086211 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.101236 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087188 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083567 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098532 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089806 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077744 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084128 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095982 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076807 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076432 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082514 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092450 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069663 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056209 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035644 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085348 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085511 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085762 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074605 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085904 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081342 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070847 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086638 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.453380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081773 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.246799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083665 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089953 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084165 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091531 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032089 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079701 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082239 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089215 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.121418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037557 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086264 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095083 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084729 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079355 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077492 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078646 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078060 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078231 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085082 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.315336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081675 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253012 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074797 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161697 
running iteration: 1221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.122106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085724 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093383 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.150447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.318090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085115 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161744 
running iteration: 1227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161743 
running iteration: 1230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073143 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161722 
running iteration: 1233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033819 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.143530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077458 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069930 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071854 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078830 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080271 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085550 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079393 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068133 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088640 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080520 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027270 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054529 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077717 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076399 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106955 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079066 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089449 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079103 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072361 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071313 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080924 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.427674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074154 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062318 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16165  test's binary_logloss:0.161699 
running iteration: 1272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093142 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089595 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161697 
running iteration: 1275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.16163  test's binary_logloss:0.161687 
running iteration: 1278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090023 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094593 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161743 
running iteration: 1281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087203 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091764 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.278853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161742 
running iteration: 1284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090555 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080725 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161721 
running iteration: 1287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.342724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076138 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081568 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074489 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070413 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081352 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073556 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084672 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080500 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077708 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078616 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075270 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079595 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032088 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.323944 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092314 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090193 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081794 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079607 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063428 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.025625 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073620 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094565 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085412 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077019 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071276 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259785 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087605 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068568 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085715 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072488 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029129 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076760 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068315 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078875 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161637 test's binary_logloss:0.161696 
running iteration: 1332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.164803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077580 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074230 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161739 
running iteration: 1338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071442 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074217 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075365 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034464 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034060 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082686 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075373 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077870 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087547 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075326 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076748 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085177 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.124498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075007 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092575 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080453 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077902 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079977 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077892 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033958 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080037 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078832 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074648 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.330259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077639 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083335 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077402 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079270 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033126 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266958 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092165 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161696 
running iteration: 1386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081063 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082613 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087084 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16174 
running iteration: 1389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077036 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091251 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071977 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070795 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080945 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088804 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070934 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033694 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028143 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054746 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.335705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088249 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091867 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086165 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106820 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084709 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081001 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263944 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083707 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075249 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038686 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262933 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080355 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075833 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075743 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076618 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082744 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079475 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079750 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074992 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033360 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.060699 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084347 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075661 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161666 test's binary_logloss:0.161729 
running iteration: 1434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073645 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077894 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078268 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088651 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058436 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039957 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161636 test's binary_logloss:0.161695 
running iteration: 1440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.335201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.16174 
running iteration: 1443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083803 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083876 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069984 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077111 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.060822 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161642 test's binary_logloss:0.161718 
running iteration: 1449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030511 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078968 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159097 test's binary_logloss:0.159147 
[2]:    train's binary_logloss:0.161673 test's binary_logloss:0.161747 
running iteration: 1452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090680 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081063 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072864 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074335 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075483 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067689 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085605 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.177856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035014 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084262 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080533 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094892 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.270508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081176 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084393 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086949 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081656 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090861 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084989 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093910 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032923 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078360 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095343 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083306 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082570 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076885 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.315514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073988 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075715 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077723 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079753 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076042 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079025 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073232 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033296 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080531 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.305862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093389 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079887 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089467 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085402 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079387 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078115 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.284860 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077636 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080022 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034362 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093671 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082393 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088026 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078649 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098004 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075649 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078399 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076357 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064876 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.119669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078804 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077700 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093005 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077425 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086241 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106957 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077671 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067132 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.117073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.120855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080181 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077906 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252012 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.444197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073382 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079194 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082723 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 1554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085316 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083869 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.318208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084677 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089306 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088471 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095053 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084530 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083992 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082378 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087948 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079959 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.246980 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073960 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032078 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029505 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092268 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080679 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074223 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076533 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084534 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077344 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067460 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.211685 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035718 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074933 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083526 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081851 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077386 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083062 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 1596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 1599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082492 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.116188 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 1602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082034 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 1605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076230 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079112 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.417084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 1608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084376 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080026 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078922 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 1611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094704 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078484 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087153 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085101 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037755 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081503 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083560 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.060613 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028951 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077853 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076021 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084959 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.103399 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072920 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258593 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079951 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080541 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075071 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081755 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094454 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081149 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082267 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.110474 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082317 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.101591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273559 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082141 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076219 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032766 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.309284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074702 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089416 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088743 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080860 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 1662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078154 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080967 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.106891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082112 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086910 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081598 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.306620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.118308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261983 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082324 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080727 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073492 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083276 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082551 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077006 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.337355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077637 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082117 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029164 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.201159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078310 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271034 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.452270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256087 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077311 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081710 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099284 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076023 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090071 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.318084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 1704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083976 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079290 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078114 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 1707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032698 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035072 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077043 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 1710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.325865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074566 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069217 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094922 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.114191 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.133495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091616 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258785 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072624 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 1722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084244 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071780 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 1725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077142 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092660 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 1728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266057 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075972 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074834 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077261 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074658 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073190 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034884 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.117652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065966 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086931 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076531 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081382 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077528 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079462 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074370 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083071 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080726 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084372 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082767 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074939 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079478 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073894 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 1761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080074 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087256 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 1764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.288890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 1767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084259 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084075 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067366 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 1770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.137030 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080219 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071502 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 1773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073390 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.052727 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 1776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085284 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072535 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 1779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094402 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 1782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065710 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083347 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081141 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076647 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.281128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092365 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094294 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069621 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080940 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079437 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079139 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034230 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.047037 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078462 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089618 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075592 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063043 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 1812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029093 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075250 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.104500 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089865 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073567 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085579 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072251 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 1821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081135 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071984 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032628 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054120 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076777 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.237068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084917 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080189 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032392 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034573 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.240345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263951 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.234343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077947 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079974 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076122 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077472 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058197 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028033 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079686 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076834 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073847 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034076 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034065 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069900 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.235127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071621 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083260 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080225 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075820 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028509 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028759 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.233196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071953 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031489 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 1869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032418 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035774 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077322 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 1872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073193 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078967 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063976 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076268 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082723 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033850 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032959 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 1881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032214 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.048133 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069875 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074748 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076909 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078953 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075858 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072445 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032749 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029407 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071212 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077224 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077770 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074625 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109126 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026244 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073973 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073962 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029169 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069799 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071927 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069986 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070274 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249683 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.132793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1916[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029945 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1917[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076743 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1918[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075607 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1919[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088934 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 1920[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1921[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029637 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1922[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027942 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 1923[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075999 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1924[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081706 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1925[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073085 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 1926[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1927[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077655 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1928[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 1929[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1930[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074303 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1931[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 1932[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1933[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1934[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070389 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 1935[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077948 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1936[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.231168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1937[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 1938[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034069 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1939[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1940[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029218 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 1941[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072779 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1942[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1943[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075289 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 1944[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075474 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1945[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069175 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1946[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074986 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1947[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1948[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030295 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1949[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035513 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1950[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080942 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1951[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078683 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1952[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069622 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1953[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080116 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1954[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.239893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1955[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076515 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1956[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074677 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1957[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1958[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1959[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033240 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1960[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1961[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1962[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072755 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1963[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253960 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1964[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030473 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1965[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037225 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1966[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1967[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074994 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1968[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1969[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1970[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082682 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1971[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071375 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1972[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070588 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1973[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031239 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1974[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082713 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1975[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077148 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1976[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.230911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 1977[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1978[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074468 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1979[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 1980[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034571 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1981[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1982[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070016 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 1983[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073064 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1984[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073346 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1985[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 1986[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079207 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1987[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.131725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1988[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031553 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 1989[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033268 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1990[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079616 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1991[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077771 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 1992[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074935 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1993[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086212 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1994[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079488 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 1995[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075862 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1996[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086897 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1997[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.369875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 1998[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.100417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 1999[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030412 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2000[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081220 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2001[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074404 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2002[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077988 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2003[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077308 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2004[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071180 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2005[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073931 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2006[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079183 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2007[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.098654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2008[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2009[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.141692 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2010[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083376 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2011[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2012[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2013[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031843 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2014[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2015[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.246730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2016[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.236401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2017[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.238192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2018[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071331 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2019[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2020[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078894 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2021[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056033 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2022[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032150 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2023[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036703 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2024[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070653 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2025[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078135 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2026[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.232442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2027[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2028[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087800 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2029[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2030[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074433 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2031[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069879 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2032[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087361 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2033[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028754 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2034[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027870 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2035[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2036[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.110210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2037[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2038[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.133495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2039[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088731 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2040[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.100233 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2041[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.301245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2042[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.119912 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2043[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.134140 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2044[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2045[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088356 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2046[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.318331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2047[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073750 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2048[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079509 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2049[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077667 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2050[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094225 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2051[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075111 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2052[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098256 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2053[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068314 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2054[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040207 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2055[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031783 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2056[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.103757 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2057[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2058[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087676 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2059[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.101085 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2060[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075840 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2061[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.102285 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2062[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070131 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2063[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2064[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078192 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2065[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075016 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2066[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078232 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2067[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059456 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2068[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032900 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2069[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033372 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2070[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254028 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2071[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072668 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2072[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064515 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2073[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078246 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2074[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2075[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031687 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2076[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033135 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2077[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078105 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2078[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.233785 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2079[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071717 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2080[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027812 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2081[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2082[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2083[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073698 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2084[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077409 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2085[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080501 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2086[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071027 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2087[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077119 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2088[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2089[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078731 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2090[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.179305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2091[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2092[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030243 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2093[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077179 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2094[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074588 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2095[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2096[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074521 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2097[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2098[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.237408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2099[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.246944 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2100[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2101[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069390 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2102[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.123581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2103[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032555 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2104[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030162 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2105[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.233856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2106[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2107[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2108[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.229396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2109[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083040 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2110[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069820 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2111[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072330 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2112[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2113[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.311070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2114[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2115[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2116[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2117[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2118[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076974 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2119[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092925 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2120[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078758 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2121[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2122[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031019 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2123[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037296 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2124[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2125[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2126[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074420 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2127[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2128[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078391 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2129[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027184 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2130[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032631 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2131[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2132[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2133[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.310451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2134[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.229339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2135[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083828 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2136[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073255 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2137[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070394 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2138[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076997 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2139[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082457 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2140[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073556 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2141[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2142[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026007 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2143[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2144[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2145[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2146[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077474 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2147[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074661 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2148[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077063 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2149[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2150[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085454 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2151[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.147727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2152[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2153[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2154[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080714 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2155[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090019 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2156[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074528 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2157[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027134 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2158[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2159[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2160[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078267 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2161[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2162[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086497 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2163[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071632 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2164[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066606 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2165[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074987 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2166[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081927 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2167[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2168[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2169[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2170[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036259 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2171[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2172[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076284 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2173[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073350 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2174[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081165 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2175[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074027 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2176[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2177[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2178[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2179[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.025718 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2180[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2181[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2182[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075952 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2183[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2184[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2185[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037451 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2186[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027602 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2187[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075096 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2188[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2189[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.241094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2190[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083580 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2191[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090601 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2192[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072765 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2193[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062837 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2194[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032762 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2195[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2196[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2197[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2198[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082194 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2199[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074537 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2200[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074955 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2201[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068553 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2202[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2203[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031863 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2204[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028534 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2205[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084083 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2206[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2207[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.243547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2208[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084589 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2209[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077703 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2210[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085160 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2211[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079698 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2212[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030768 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2213[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.106581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2214[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028756 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2215[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2216[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077258 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2217[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073149 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2218[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2219[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076758 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2220[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.239630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2221[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086324 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2222[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078386 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2223[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073031 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2224[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085827 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2225[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033513 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2226[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033658 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2227[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063614 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2228[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079116 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2229[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090851 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2230[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084945 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2231[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083864 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2232[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.131478 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2233[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.293714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2234[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077929 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2235[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072961 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2236[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082802 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2237[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.104777 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2238[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2239[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.117485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2240[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.386224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2241[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080657 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2242[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081172 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2243[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082489 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2244[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2245[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2246[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.102439 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2247[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2248[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093980 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2249[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085534 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2250[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080260 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2251[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.294300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2252[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066227 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2253[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038568 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2254[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074541 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2255[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078148 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2256[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076977 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2257[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076430 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2258[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081154 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2259[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085451 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2260[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079286 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2261[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075146 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2262[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073551 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2263[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085689 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2264[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2265[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2266[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076639 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2267[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075837 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2268[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071478 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2269[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070529 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2270[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2271[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2272[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2273[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086691 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2274[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089671 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2275[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.295926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2276[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083550 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2277[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070087 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2278[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030083 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2279[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2280[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2281[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071350 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2282[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079015 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2283[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074512 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2284[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079857 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2285[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2286[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2287[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079907 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2288[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068947 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2289[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2290[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065159 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2291[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2292[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2293[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.131193 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2294[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078150 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2295[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2296[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.355262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2297[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087711 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2298[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2299[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2300[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034795 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2301[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034201 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2302[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079936 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2303[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085180 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2304[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075389 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2305[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2306[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090292 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2307[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073860 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2308[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080526 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2309[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032142 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2310[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038808 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2311[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075061 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2312[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075809 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2313[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254059 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2314[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2315[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073137 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2316[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2317[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080942 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2318[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088979 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2319[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031727 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2320[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054924 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2321[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2322[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.244701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2323[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075145 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2324[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2325[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081826 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2326[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076696 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2327[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087102 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2328[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073762 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2329[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084667 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2330[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034939 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2331[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028604 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2332[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079983 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2333[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070810 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2334[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083234 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2335[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2336[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2337[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081561 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2338[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075781 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2339[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076212 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2340[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059304 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2341[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2342[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2343[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.120308 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2344[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070797 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2345[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082678 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2346[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075436 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2347[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081445 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2348[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081897 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2349[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2350[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061436 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2351[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.109192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2352[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066799 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2353[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2354[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075699 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2355[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077016 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2356[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2357[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084589 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2358[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086332 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2359[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.273101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2360[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086632 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2361[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.121717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2362[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036051 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2363[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2364[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2365[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089063 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2366[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073407 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2367[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073315 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2368[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2369[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.313163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2370[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084330 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2371[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2372[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2373[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084898 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2374[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072432 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2375[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.113937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2376[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2377[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077248 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2378[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078489 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2379[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080141 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2380[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037964 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2381[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029753 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2382[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.348020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2383[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075727 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2384[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097799 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2385[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.270821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2386[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2387[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072968 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2388[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077274 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2389[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263806 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2390[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078707 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2391[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2392[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2393[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082811 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2394[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2395[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252123 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2396[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.268729 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2397[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030516 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2398[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035969 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2399[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2400[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254970 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2401[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.138528 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2402[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081867 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2403[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081931 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2404[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099237 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2405[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2406[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085656 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2407[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075907 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2408[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070649 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2409[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026625 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2410[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2411[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.330481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2412[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2413[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075619 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2414[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2415[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2416[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075425 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2417[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091312 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2418[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2419[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079700 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2420[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080017 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2421[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.375679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2422[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031783 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2423[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108721 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2424[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081267 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2425[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.337620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2426[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082596 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2427[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074467 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2428[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085942 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2429[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2430[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2431[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2432[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076910 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2433[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090767 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2434[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075991 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2435[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083702 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2436[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.045822 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2437[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076522 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2438[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088739 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2439[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067983 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2440[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071066 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2441[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074357 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2442[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076136 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2443[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092487 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2444[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2445[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065323 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2446[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034910 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2447[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032204 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2448[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073894 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2449[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080861 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2450[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2451[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097053 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2452[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079007 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2453[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2454[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085515 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2455[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089623 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2456[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077966 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2457[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079385 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2458[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2459[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030391 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2460[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031850 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2461[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080349 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2462[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2463[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2464[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.329783 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2465[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2466[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2467[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089928 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2468[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087322 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2469[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077872 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2470[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088903 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2471[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2472[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2473[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033914 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2474[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.053902 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2475[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088262 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2476[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.248069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2477[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087166 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161672 test's binary_logloss:0.16175 
running iteration: 2478[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.281567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2479[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.375537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2480[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093327 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2481[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077714 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2482[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078399 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2483[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079105 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2484[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029491 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2485[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032318 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2486[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2487[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264049 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2488[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083058 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2489[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2490[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078908 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2491[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095046 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2492[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2493[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069329 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2494[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.093716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2495[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073376 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2496[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.270184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2497[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2498[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075310 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2499[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073993 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2500[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.264165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2501[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031652 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2502[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039862 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2503[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249838 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2504[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072501 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2505[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077420 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2506[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2507[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071297 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2508[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2509[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.361238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2510[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.294846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2511[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2512[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086722 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2513[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.291454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.1617 
running iteration: 2514[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077866 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2515[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084737 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2516[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038233 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2517[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073348 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2518[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096669 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2519[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082499 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161635 test's binary_logloss:0.16169 
running iteration: 2520[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077142 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2521[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2522[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161683 test's binary_logloss:0.161748 
running iteration: 2523[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082683 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2524[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2525[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074895 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161747 
running iteration: 2526[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2527[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.041339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2528[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034214 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161661 test's binary_logloss:0.161732 
running iteration: 2529[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.342541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2530[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074553 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2531[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2532[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077734 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2533[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082246 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2534[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080416 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2535[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.284704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2536[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081992 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2537[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075291 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2538[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079723 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2539[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2540[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2541[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091264 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2542[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079704 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2543[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069174 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2544[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2545[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085564 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2546[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080678 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2547[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2548[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2549[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073770 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2550[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077657 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2551[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080313 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2552[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089355 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2553[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092211 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2554[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079308 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2555[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077200 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2556[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.247962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2557[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073541 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2558[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081903 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2559[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073873 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2560[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077299 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2561[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.272658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2562[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033825 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2563[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034300 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2564[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2565[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.235955 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2566[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2567[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079877 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161649 test's binary_logloss:0.161699 
running iteration: 2568[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085811 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2569[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088652 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2570[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077419 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161646 test's binary_logloss:0.161698 
running iteration: 2571[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089938 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2572[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084761 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2573[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159086 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161634 test's binary_logloss:0.16169 
running iteration: 2574[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085146 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2575[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.108517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2576[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083231 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.161682 test's binary_logloss:0.161747 
running iteration: 2577[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074760 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2578[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2579[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076604 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16168  test's binary_logloss:0.161746 
running iteration: 2580[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075914 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2581[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081161 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2582[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086414 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159158 
[2]:    train's binary_logloss:0.16166  test's binary_logloss:0.161732 
running iteration: 2583[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080922 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2584[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.322048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2585[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2586[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080773 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2587[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033202 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2588[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096802 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.16167  test's binary_logloss:0.161749 
running iteration: 2589[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079893 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2590[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078516 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2591[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083845 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161639 test's binary_logloss:0.161731 
running iteration: 2592[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082792 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2593[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077365 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2594[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.242817 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2595[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074667 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2596[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088276 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2597[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2598[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.037395 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2599[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070885 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2600[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081217 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2601[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086864 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2602[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.266122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2603[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.245429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2604[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085754 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2605[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099071 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2606[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.437137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2607[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086621 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2608[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073983 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2609[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085598 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2610[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072299 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2611[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.138101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2612[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032400 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2613[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078662 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2614[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2615[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076938 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2616[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2617[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078599 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2618[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250038 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2619[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2620[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2621[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.291981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2622[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2623[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081832 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2624[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099522 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2625[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2626[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2627[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2628[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.239025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2629[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084549 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2630[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087629 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2631[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2632[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080116 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2633[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085215 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161746 
running iteration: 2634[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2635[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2636[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.100997 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2637[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077308 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2638[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079253 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2639[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075810 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2640[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077805 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2641[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086559 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2642[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077358 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2643[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078948 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2644[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.253317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2645[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096436 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2646[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073761 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2647[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083932 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2648[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.324324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2649[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.112089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2650[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2651[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.325451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2652[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080098 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2653[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.277568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2654[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.094408 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2655[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.274480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2656[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2657[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075220 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2658[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077157 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2659[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082945 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2660[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073285 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2661[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090568 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2662[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2663[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033855 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2664[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083260 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2665[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.067207 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2666[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077096 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2667[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084604 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2668[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079177 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2669[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075597 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2670[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085739 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2671[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.320307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2672[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2673[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081543 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2674[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2675[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069103 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161648 test's binary_logloss:0.161698 
running iteration: 2676[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084024 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2677[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080783 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2678[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075060 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161697 
running iteration: 2679[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2680[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082359 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2681[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033677 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161689 
running iteration: 2682[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081135 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2683[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.291704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2684[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161747 
running iteration: 2685[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090901 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2686[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085091 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2687[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078006 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2688[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034655 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2689[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.278007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2690[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075752 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159107 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2691[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076671 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2692[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080785 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2693[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078726 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161671 test's binary_logloss:0.161749 
running iteration: 2694[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081316 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2695[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074156 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2696[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161748 
running iteration: 2697[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.110925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2698[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030549 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2699[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082627 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159094 test's binary_logloss:0.159163 
[2]:    train's binary_logloss:0.161638 test's binary_logloss:0.16173 
running iteration: 2700[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077895 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2701[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2702[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2703[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2704[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2705[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2706[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087034 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2707[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.119809 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2708[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2709[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.267690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2710[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.090155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2711[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2712[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086580 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2713[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086251 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2714[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2715[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.124287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2716[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086624 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2717[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2718[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2719[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.277215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2720[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2721[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083429 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2722[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.123108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2723[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077073 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2724[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039855 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2725[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074885 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2726[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085817 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2727[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2728[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2729[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.269760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161698 
running iteration: 2730[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078637 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2731[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076587 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2732[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.250362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161645 test's binary_logloss:0.161696 
running iteration: 2733[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2734[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072180 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2735[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036499 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159085 test's binary_logloss:0.159125 
[2]:    train's binary_logloss:0.161633 test's binary_logloss:0.161688 
running iteration: 2736[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.146098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2737[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2738[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074014 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161746 
running iteration: 2739[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.092119 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2740[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.251703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2741[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081505 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161745 
running iteration: 2742[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084965 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2743[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077704 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2744[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.068011 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159106 test's binary_logloss:0.159157 
[2]:    train's binary_logloss:0.161659 test's binary_logloss:0.161731 
running iteration: 2745[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033455 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2746[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071737 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2747[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161669 test's binary_logloss:0.161739 
running iteration: 2748[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085615 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2749[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.265480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2750[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161668 test's binary_logloss:0.161738 
running iteration: 2751[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077549 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2752[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077097 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2753[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089805 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159093 test's binary_logloss:0.159143 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161721 
running iteration: 2754[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034598 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2755[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086823 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2756[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082856 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2757[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2758[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2759[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081400 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2760[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.072111 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2761[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077335 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2762[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080089 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2763[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.118044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2764[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073945 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2765[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2766[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2767[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.261243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2768[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074482 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2769[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078359 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2770[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080401 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2771[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078866 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2772[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075089 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2773[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.105146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2774[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2775[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079688 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2776[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.096183 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2777[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080727 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2778[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083100 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2779[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034215 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2780[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082505 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2781[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080779 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2782[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085722 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2783[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080297 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.16173 
running iteration: 2784[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084639 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2785[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034995 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2786[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084578 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2787[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2788[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.105495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2789[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080887 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2790[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.263286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2791[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081542 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2792[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161743 
running iteration: 2793[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030650 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2794[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.300892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2795[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095958 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2796[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086489 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2797[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076638 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2798[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081974 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2799[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085400 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2800[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.098255 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2801[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085224 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2802[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2803[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035611 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2804[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079036 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2805[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2806[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083846 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2807[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084793 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2808[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074010 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2809[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079533 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2810[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2811[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062029 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2812[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030776 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2813[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085655 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2814[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069381 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2815[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.276612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2816[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081222 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2817[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.073384 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2818[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2819[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.119724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2820[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088364 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2821[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.070641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2822[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077281 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2823[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2824[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2825[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086644 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2826[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085929 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2827[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2828[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071622 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2829[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064832 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2830[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031961 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2831[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077140 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2832[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081209 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2833[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081998 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2834[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076291 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2835[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074241 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2836[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.081649 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2837[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.239839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2838[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2839[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038342 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2840[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080930 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161719 
running iteration: 2841[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2842[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083509 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2843[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.082579 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161641 test's binary_logloss:0.161698 
running iteration: 2844[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079430 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2845[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074849 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2846[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.282217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2847[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2848[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080008 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2849[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.069603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2850[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2851[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2852[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078746 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161727 
running iteration: 2853[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2854[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2855[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.339652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2856[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088625 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2857[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.083073 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2858[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.123144 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2859[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076746 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2860[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.099182 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2861[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2862[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079636 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2863[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.071088 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2864[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.366023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2865[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079025 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2866[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080877 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2867[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.111179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2868[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.030417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2869[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078821 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2870[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084396 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2871[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.256961 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2872[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077706 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2873[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.260078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2874[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078181 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2875[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079981 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2876[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.121260 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2877[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2878[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2879[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078079 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2880[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.298734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2881[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.085633 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2882[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2883[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056778 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2884[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076013 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2885[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078905 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2886[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.279413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2887[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.080436 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2888[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2889[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.249964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2890[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.257546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2891[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161667 test's binary_logloss:0.161729 
running iteration: 2892[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.036833 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2893[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2894[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.258037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.161653 test's binary_logloss:0.161718 
running iteration: 2895[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.095906 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2896[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2897[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.275131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159095 test's binary_logloss:0.159136 
[2]:    train's binary_logloss:0.16164  test's binary_logloss:0.161697 
running iteration: 2898[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2899[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.259478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2900[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.252227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161677 test's binary_logloss:0.161742 
running iteration: 2901[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.074891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2902[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.344705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2903[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.086845 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161675 test's binary_logloss:0.161741 
running iteration: 2904[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.255974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2905[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.088903 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2906[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089131 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159105 test's binary_logloss:0.159156 
[2]:    train's binary_logloss:0.161655 test's binary_logloss:0.161726 
running iteration: 2907[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.205492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2908[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040159 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2909[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.078176 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.1591   test's binary_logloss:0.159145 
[2]:    train's binary_logloss:0.161681 test's binary_logloss:0.161741 
running iteration: 2910[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.091345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2911[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.089604 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2912[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077230 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161679 test's binary_logloss:0.161751 
running iteration: 2913[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.087652 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2914[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.084921 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2915[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.077267 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159099 test's binary_logloss:0.159148 
[2]:    train's binary_logloss:0.161647 test's binary_logloss:0.161726 
running iteration: 2916

 #optimal parameters
min(perf2)

[1] 0.1591216

lgb2.gridS[which.min(perf2),]

cat("Choose Model",which.min(perf2))

Choose Model 109

k=which.min(perf2)
lgb2.grid=list(objective = "binary",
                metric="binary_logloss",
                min_sum_hessian_in_leaf=lgb2.gridS[k,"min_sum_hessian_in_leaf"],
                feature_fraction =lgb2.gridS[k,"feature_fraction"], 
                bagging_fraction =lgb2.gridS[k,"bagging_fraction"], 
                bagging_freq =lgb2.gridS[k,"bagging_freq"], 
                lambda_l1 =lgb2.gridS[k,"lambda_l1"], 
                lambda_l2 = lgb2.gridS[k,"lambda_l2"], 
                min_data_in_bin=lgb2.gridS[k,"min_data_in_bin"],
                min_gain_to_split =lgb2.gridS[k,"min_gain_to_split"], 
                min_data_in_leaf = lgb2.gridS[k,"min_data_in_leaf"],
                is_unbalance=as.logical(1-p))


lgb2 =lightgbm(params =lgb2.grid ,
                data=lgb2.train_mat,
                learning_rate=0.02,
                early_stopping_rounds=10, 
                num_leaves = 15,
                valids=valid2, 
                nrounds =lgb2[["best_iter"]])

[LightGBM] [Info] Number of positive: 10950, number of negative: 284738
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.075398 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1522
[LightGBM] [Info] Number of data points in the train set: 295688, number of used features: 173
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037032 -> initscore=-3.258230
[LightGBM] [Info] Start training from score -3.258230
[1]:    train's binary_logloss:0.159089 test's binary_logloss:0.159122 

saveRDS.lgb.Booster(lgb2,"unb_ori_lgb.rds")
#varimp----------------
lgb2.imp=lgb.importance(lgb2,percentage = TRUE)
lgb.plot.importance(lgb2.imp)

#LGB2 evaluation------------
lgb2.p=predict(lgb2,lgb2.val)
lgb2.pred=prediction(lgb2.p,as.factor(val$target))
lgb2.perf=performance(lgb2.pred,"f")
plot(lgb2.perf) #colorize=T
abline(a=0,b=1)

LogLoss(lgb2.p,val$target)

[1] 0.1591216

summary(lgb2.p)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.04346 0.04602 0.04720 0.04714 0.04879 0.05486 

p_conso=data.frame("lgb1.p"=lgb1.p,"lgb2.p"=lgb2.p)
write.csv(p_conso,"C_p_conso.csv")

library(keras)

#ANN using PCA--------------------------
tr.nmat1=as.matrix(train.pca)
mode(tr.nmat1)="numeric"
dimnames(tr.nmat1)=NULL
tr.nmat1[,-1]=normalize(tr.nmat1[,-1]) #input must be numeric
dim(tr.nmat1)

[1] 83288    34

val.nmat1=as.matrix(val.pca)
mode(val.nmat1)="numeric"
dimnames(val.nmat1)=NULL
val.nmat1[,-1]=normalize(val.nmat1[,-1])
dim(val.nmat1)

[1] 41643    34

#model architecture
nn1=keras_model_sequential()
nn1%>%
        layer_dense(units = 10, activation = 'relu', input_shape = c(33)) %>%
        layer_dropout(rate = 0.2) %>% 
        layer_dense(units = 1, activation = 'sigmoid') #softmax does not work
summary(nn1) # n(hlnode)xn(inlnode)+(biases=n(hlnode)

Model: "sequential_4"
___________________________________________________________________
Layer (type)                  Output Shape              Param #    
===================================================================
dense_8 (Dense)               (None, 10)                340        
___________________________________________________________________
dropout_4 (Dropout)           (None, 10)                0          
___________________________________________________________________
dense_9 (Dense)               (None, 1)                 11         
===================================================================
Total params: 351
Trainable params: 351
Non-trainable params: 0
___________________________________________________________________

#Compiling
nn1 %>%
compile(loss = "binary_crossentropy",
optimizer =optimizer_adam(lr=0.0001),
metrics = "binary_accuracy")

#Fitting model
nn1.h=nn1 %>% #training history
fit(tr.nmat1[,-1],
tr.nmat1[,1],
epochs = 100,#till leveling and minimal divergence, for integer coded=50
batch_size = 128,
validation_split = 1/3,
class_weight=list("0"=1,"1"=1.1)) #  play with weights

Epoch 1/100

  1/434 [..............................] - ETA: 0s - loss: 0.6592 - binary_accuracy: 0.7109
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232/434 [===============>..............] - ETA: 0s - loss: 0.6412 - binary_accuracy: 0.7915
251/434 [================>.............] - ETA: 0s - loss: 0.6398 - binary_accuracy: 0.7956
269/434 [=================>............] - ETA: 0s - loss: 0.6384 - binary_accuracy: 0.8004
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346/434 [======================>.......] - ETA: 0s - loss: 0.6319 - binary_accuracy: 0.8193
365/434 [========================>.....] - ETA: 0s - loss: 0.6301 - binary_accuracy: 0.8237
386/434 [=========================>....] - ETA: 0s - loss: 0.6283 - binary_accuracy: 0.8284
405/434 [==========================>...] - ETA: 0s - loss: 0.6266 - binary_accuracy: 0.8322
424/434 [============================>.] - ETA: 0s - loss: 0.6251 - binary_accuracy: 0.8358
434/434 [==============================] - 1s 3ms/step - loss: 0.6244 - binary_accuracy: 0.8377

434/434 [==============================] - 3s 6ms/step - loss: 0.6244 - binary_accuracy: 0.8377 - val_loss: 0.5823 - val_binary_accuracy: 0.9377
Epoch 2/100

  1/434 [..............................] - ETA: 0s - loss: 0.5873 - binary_accuracy: 0.8906
 34/434 [=>............................] - ETA: 0s - loss: 0.5846 - binary_accuracy: 0.9210
 69/434 [===>..........................] - ETA: 0s - loss: 0.5812 - binary_accuracy: 0.9286
109/434 [======>.......................] - ETA: 0s - loss: 0.5776 - binary_accuracy: 0.9318
144/434 [========>.....................] - ETA: 0s - loss: 0.5756 - binary_accuracy: 0.9321
182/434 [===========>..................] - ETA: 0s - loss: 0.5725 - binary_accuracy: 0.9342
217/434 [==============>...............] - ETA: 0s - loss: 0.5689 - binary_accuracy: 0.9363
253/434 [================>.............] - ETA: 0s - loss: 0.5658 - binary_accuracy: 0.9384
287/434 [==================>...........] - ETA: 0s - loss: 0.5630 - binary_accuracy: 0.9396
318/434 [====================>.........] - ETA: 0s - loss: 0.5603 - binary_accuracy: 0.9410
359/434 [=======================>......] - ETA: 0s - loss: 0.5568 - binary_accuracy: 0.9426
404/434 [==========================>...] - ETA: 0s - loss: 0.5534 - binary_accuracy: 0.9436
434/434 [==============================] - 1s 1ms/step - loss: 0.5508 - binary_accuracy: 0.9442

434/434 [==============================] - 1s 3ms/step - loss: 0.5508 - binary_accuracy: 0.9442 - val_loss: 0.5107 - val_binary_accuracy: 0.9532
Epoch 3/100

  1/434 [..............................] - ETA: 0s - loss: 0.4977 - binary_accuracy: 0.9688
 34/434 [=>............................] - ETA: 0s - loss: 0.5144 - binary_accuracy: 0.9522
 71/434 [===>..........................] - ETA: 0s - loss: 0.5111 - binary_accuracy: 0.9530
106/434 [======>.......................] - ETA: 0s - loss: 0.5079 - binary_accuracy: 0.9535
140/434 [========>.....................] - ETA: 0s - loss: 0.5054 - binary_accuracy: 0.9544
173/434 [==========>...................] - ETA: 0s - loss: 0.5023 - binary_accuracy: 0.9556
207/434 [=============>................] - ETA: 0s - loss: 0.4995 - binary_accuracy: 0.9558
241/434 [===============>..............] - ETA: 0s - loss: 0.4968 - binary_accuracy: 0.9557
276/434 [==================>...........] - ETA: 0s - loss: 0.4950 - binary_accuracy: 0.9550
306/434 [====================>.........] - ETA: 0s - loss: 0.4927 - binary_accuracy: 0.9550
337/434 [======================>.......] - ETA: 0s - loss: 0.4909 - binary_accuracy: 0.9546
372/434 [========================>.....] - ETA: 0s - loss: 0.4881 - binary_accuracy: 0.9548
407/434 [===========================>..] - ETA: 0s - loss: 0.4855 - binary_accuracy: 0.9548
434/434 [==============================] - 1s 2ms/step - loss: 0.4836 - binary_accuracy: 0.9550

434/434 [==============================] - 2s 4ms/step - loss: 0.4836 - binary_accuracy: 0.9550 - val_loss: 0.4447 - val_binary_accuracy: 0.9534
Epoch 4/100

  1/434 [..............................] - ETA: 0s - loss: 0.4446 - binary_accuracy: 0.9688
 19/434 [>.............................] - ETA: 1s - loss: 0.4485 - binary_accuracy: 0.9581
 36/434 [=>............................] - ETA: 1s - loss: 0.4485 - binary_accuracy: 0.9555
 53/434 [==>...........................] - ETA: 1s - loss: 0.4502 - binary_accuracy: 0.9533
 71/434 [===>..........................] - ETA: 1s - loss: 0.4484 - binary_accuracy: 0.9536
 87/434 [=====>........................] - ETA: 1s - loss: 0.4489 - binary_accuracy: 0.9525
107/434 [======>.......................] - ETA: 0s - loss: 0.4476 - binary_accuracy: 0.9521
126/434 [=======>......................] - ETA: 0s - loss: 0.4454 - binary_accuracy: 0.9531
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161/434 [==========>...................] - ETA: 0s - loss: 0.4422 - binary_accuracy: 0.9538
179/434 [===========>..................] - ETA: 0s - loss: 0.4409 - binary_accuracy: 0.9538
196/434 [============>.................] - ETA: 0s - loss: 0.4395 - binary_accuracy: 0.9541
213/434 [=============>................] - ETA: 0s - loss: 0.4382 - binary_accuracy: 0.9543
230/434 [==============>...............] - ETA: 0s - loss: 0.4366 - binary_accuracy: 0.9549
247/434 [================>.............] - ETA: 0s - loss: 0.4352 - binary_accuracy: 0.9550
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434/434 [==============================] - 1s 3ms/step - loss: 0.4221 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.4221 - binary_accuracy: 0.9552 - val_loss: 0.3857 - val_binary_accuracy: 0.9534
Epoch 5/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.3692 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.3692 - binary_accuracy: 0.9552 - val_loss: 0.3350 - val_binary_accuracy: 0.9534
Epoch 6/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.3251 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.3251 - binary_accuracy: 0.9552 - val_loss: 0.2936 - val_binary_accuracy: 0.9534
Epoch 7/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2894 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2894 - binary_accuracy: 0.9552 - val_loss: 0.2610 - val_binary_accuracy: 0.9534
Epoch 8/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2621 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2621 - binary_accuracy: 0.9552 - val_loss: 0.2365 - val_binary_accuracy: 0.9534
Epoch 9/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2421 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2421 - binary_accuracy: 0.9552 - val_loss: 0.2189 - val_binary_accuracy: 0.9534
Epoch 10/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2282 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2282 - binary_accuracy: 0.9552 - val_loss: 0.2068 - val_binary_accuracy: 0.9534
Epoch 11/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2188 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 3ms/step - loss: 0.2188 - binary_accuracy: 0.9552 - val_loss: 0.1988 - val_binary_accuracy: 0.9534
Epoch 12/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2123 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2123 - binary_accuracy: 0.9552 - val_loss: 0.1937 - val_binary_accuracy: 0.9534
Epoch 13/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2078 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2078 - binary_accuracy: 0.9552 - val_loss: 0.1906 - val_binary_accuracy: 0.9534
Epoch 14/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2057 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2057 - binary_accuracy: 0.9552 - val_loss: 0.1888 - val_binary_accuracy: 0.9534
Epoch 15/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2041 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 3ms/step - loss: 0.2041 - binary_accuracy: 0.9552 - val_loss: 0.1877 - val_binary_accuracy: 0.9534
Epoch 16/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2035 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2035 - binary_accuracy: 0.9552 - val_loss: 0.1871 - val_binary_accuracy: 0.9534
Epoch 17/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2035 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2035 - binary_accuracy: 0.9552 - val_loss: 0.1867 - val_binary_accuracy: 0.9534
Epoch 18/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2041 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2041 - binary_accuracy: 0.9552 - val_loss: 0.1864 - val_binary_accuracy: 0.9534
Epoch 19/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2028 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2028 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 20/100

  1/434 [..............................] - ETA: 0s - loss: 0.1688 - binary_accuracy: 0.9609
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434/434 [==============================] - 2s 5ms/step - loss: 0.2026 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 21/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2030 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2030 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 22/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2025 - binary_accuracy: 0.9552 - val_loss: 0.1857 - val_binary_accuracy: 0.9534
Epoch 23/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2016 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 3ms/step - loss: 0.2016 - binary_accuracy: 0.9552 - val_loss: 0.1855 - val_binary_accuracy: 0.9534
Epoch 24/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1993 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1993 - binary_accuracy: 0.9552 - val_loss: 0.1855 - val_binary_accuracy: 0.9534
Epoch 25/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2010 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2010 - binary_accuracy: 0.9552 - val_loss: 0.1854 - val_binary_accuracy: 0.9534
Epoch 26/100

  1/434 [..............................] - ETA: 0s - loss: 0.2138 - binary_accuracy: 0.9531
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434/434 [==============================] - 1s 2ms/step - loss: 0.2001 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2001 - binary_accuracy: 0.9552 - val_loss: 0.1853 - val_binary_accuracy: 0.9534
Epoch 27/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2012 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2012 - binary_accuracy: 0.9552 - val_loss: 0.1852 - val_binary_accuracy: 0.9534
Epoch 28/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.2002 - binary_accuracy: 0.9552 - val_loss: 0.1852 - val_binary_accuracy: 0.9534
Epoch 29/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2009 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.2009 - binary_accuracy: 0.9552 - val_loss: 0.1851 - val_binary_accuracy: 0.9534
Epoch 30/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1995 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1995 - binary_accuracy: 0.9552 - val_loss: 0.1851 - val_binary_accuracy: 0.9534
Epoch 31/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2000 - binary_accuracy: 0.9552 - val_loss: 0.1850 - val_binary_accuracy: 0.9534
Epoch 32/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1996 - binary_accuracy: 0.9552 - val_loss: 0.1850 - val_binary_accuracy: 0.9534
Epoch 33/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2000 - binary_accuracy: 0.9552 - val_loss: 0.1850 - val_binary_accuracy: 0.9534
Epoch 34/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1998 - binary_accuracy: 0.9552 - val_loss: 0.1849 - val_binary_accuracy: 0.9534
Epoch 35/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1990 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1990 - binary_accuracy: 0.9552 - val_loss: 0.1849 - val_binary_accuracy: 0.9534
Epoch 36/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1996 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1996 - binary_accuracy: 0.9552 - val_loss: 0.1849 - val_binary_accuracy: 0.9534
Epoch 37/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1985 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1985 - binary_accuracy: 0.9552 - val_loss: 0.1849 - val_binary_accuracy: 0.9534
Epoch 38/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1980 - binary_accuracy: 0.9552 - val_loss: 0.1849 - val_binary_accuracy: 0.9534
Epoch 39/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1994 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 40/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1994 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 41/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1989 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 42/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1981 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 43/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1990 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 44/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1986 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.1986 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 45/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1980 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 46/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1985 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.1985 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 47/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1983 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 48/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1979 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 49/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1987 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1987 - binary_accuracy: 0.9552 - val_loss: 0.1848 - val_binary_accuracy: 0.9534
Epoch 50/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1983 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 51/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1986 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 52/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1975 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 53/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1974 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 54/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1981 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 55/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1982 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1982 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 56/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1984 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 57/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1981 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1981 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 58/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1975 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.1975 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 59/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1978 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 60/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1975 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 61/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1981 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1981 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 62/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1975 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 63/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1969 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 64/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1972 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1972 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 65/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1972 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 66/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1966 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 67/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1968 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 68/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 69/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1967 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 70/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1967 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 71/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1966 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 72/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1966 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 73/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1969 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 74/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 75/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1967 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 76/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1965 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 77/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 78/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1966 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 79/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 80/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1965 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 81/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1959 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 82/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 83/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 84/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1960 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 85/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 86/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1965 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 87/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1960 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 88/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1954 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 89/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1956 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 90/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 91/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1955 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 92/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1957 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 93/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1955 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 94/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1955 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1955 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 95/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1961 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 96/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1958 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1958 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 97/100

  1/434 [..............................] - ETA: 0s - loss: 0.1583 - binary_accuracy: 0.9688
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434/434 [==============================] - 1s 3ms/step - loss: 0.1964 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 98/100

  1/434 [..............................] - ETA: 0s - loss: 0.1560 - binary_accuracy: 0.9609
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434/434 [==============================] - 1s 2ms/step - loss: 0.1954 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1954 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 99/100

  1/434 [..............................] - ETA: 0s - loss: 0.1308 - binary_accuracy: 0.9766
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434/434 [==============================] - 1s 3ms/step - loss: 0.1947 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1947 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534
Epoch 100/100

  1/434 [..............................] - ETA: 0s - loss: 0.1232 - binary_accuracy: 0.9766
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434/434 [==============================] - 1s 1ms/step - loss: 0.1954 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.1954 - binary_accuracy: 0.9552 - val_loss: 0.1847 - val_binary_accuracy: 0.9534

plot(nn1.h)

saveRDS(serialize_model(model=nn1,include_optimizer = TRUE),"pca_nn_mod.rds")
#nn1=readRDS("pca_nn_mod.rds")
#nn1=unserialize_model(nn1)


nn1%>% evaluate(tr.nmat1[,-1],tr.nmat1[,1])

   1/2603 [..............................] - ETA: 0s - loss: 0.1437 - binary_accuracy: 0.9688
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2603/2603 [==============================] - 3s 1ms/step - loss: 0.1805 - binary_accuracy: 0.9546
      0.9546153 

nn1%>% evaluate(val.nmat1[,-1],val.nmat1[,1])

   1/1302 [..............................] - ETA: 0s - loss: 0.2577 - binary_accuracy: 0.9375
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1302/1302 [==============================] - 2s 1ms/step - loss: 0.1823 - binary_accuracy: 0.9546
      0.9546142 

nn1.p=nn1%>%predict_proba(val.nmat1[,-1])
#summary(nn1.p)

#nn1.cl=ifelse(nn1.p>0.07,1,0)
#table("predicted"=nn1.cl,"actual"=val.nmat1[,1])

nn1.pred=ROCR::prediction(nn1.p,val.nmat1[,1])
nn1.perf=ROCR::performance(nn1.pred,"tpr","fpr")
plot(nn1.perf,colorize=T)
abline(a=0,b=1)

#ANN using original predictors---------------
tr$target=as.factor(tr$target)
val$target=as.factor(val$target)

library(recipes)
rec_obj=recipe(target~.,data=tr[,!names(tr) %in% outlist])%>%
        step_dummy(all_nominal(), -all_outcomes())%>%
        prep(data=tr[,!names(tr) %in% outlist])
nn.tr_lab=as.numeric(as.character(tr$target))
tr.enc=bake(rec_obj, new_data =tr[,!names(tr) %in% outlist])%>%select(-target)

nn.val_lab=as.numeric(as.character(val$target))
val.enc=bake(rec_obj, new_data =val[,!names(val) %in% outlist])%>%select(-target)


#matrix conversion
tr.nmat2=as.matrix(tr.enc)
mode(tr.nmat2)="numeric"
dimnames(tr.nmat2)=NULL
tr.nmat=normalize(tr.nmat2) #input must be numeric
dim(tr.nmat2)

[1] 83288   208

val.nmat2=as.matrix(val.enc)
mode(val.nmat2)="numeric"
dimnames(val.nmat2)=NULL
val.nmat2=normalize(val.nmat2)
dim(val.nmat2)

[1] 41643   208

#Model architecture
nn2=keras_model_sequential()
nn2%>%
       layer_dense(units = 10, activation = 'relu', input_shape = c(dim(tr.nmat2)[2])) %>%
        layer_dropout(rate = 0.2) %>% 
        layer_dense(units = 1, activation = 'sigmoid') 
summary(nn2) # n(hlnode)xn(inlnode)+(biases=n(hlnode)

Model: "sequential_5"
___________________________________________________________________
Layer (type)                  Output Shape              Param #    
===================================================================
dense_10 (Dense)              (None, 10)                2090       
___________________________________________________________________
dropout_5 (Dropout)           (None, 10)                0          
___________________________________________________________________
dense_11 (Dense)              (None, 1)                 11         
===================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
___________________________________________________________________

#Compiling
nn2 %>%
compile(loss = "binary_crossentropy",
optimizer =optimizer_adam(lr=0.0001),
metrics = "binary_accuracy")

#Fitting model
nn2.h=nn2 %>% #training history
fit(tr.nmat2,
nn.tr_lab,
epochs = 100,#till leveling and minimal divergence
batch_size = 128,
validation_split = 1/3,
class_weight=list("0"=1,"1"=1.1)) #  play with weights

Epoch 1/100

  1/434 [..............................] - ETA: 0s - loss: 0.6972 - binary_accuracy: 0.4766
 42/434 [=>............................] - ETA: 0s - loss: 0.6780 - binary_accuracy: 0.5999
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434/434 [==============================] - 1s 1ms/step - loss: 0.4885 - binary_accuracy: 0.8775

434/434 [==============================] - 2s 4ms/step - loss: 0.4885 - binary_accuracy: 0.8775 - val_loss: 0.2632 - val_binary_accuracy: 0.9534
Epoch 2/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2376 - binary_accuracy: 0.9552 - val_loss: 0.1997 - val_binary_accuracy: 0.9534
Epoch 3/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2186 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2186 - binary_accuracy: 0.9552 - val_loss: 0.1951 - val_binary_accuracy: 0.9534
Epoch 4/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2150 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.2150 - binary_accuracy: 0.9552 - val_loss: 0.1932 - val_binary_accuracy: 0.9534
Epoch 5/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2137 - binary_accuracy: 0.9552 - val_loss: 0.1918 - val_binary_accuracy: 0.9534
Epoch 6/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2114 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2114 - binary_accuracy: 0.9552 - val_loss: 0.1906 - val_binary_accuracy: 0.9534
Epoch 7/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2108 - binary_accuracy: 0.9552 - val_loss: 0.1897 - val_binary_accuracy: 0.9534
Epoch 8/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2106 - binary_accuracy: 0.9552 - val_loss: 0.1889 - val_binary_accuracy: 0.9534
Epoch 9/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2083 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2083 - binary_accuracy: 0.9552 - val_loss: 0.1884 - val_binary_accuracy: 0.9534
Epoch 10/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2066 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2066 - binary_accuracy: 0.9552 - val_loss: 0.1879 - val_binary_accuracy: 0.9534
Epoch 11/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2068 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2068 - binary_accuracy: 0.9552 - val_loss: 0.1875 - val_binary_accuracy: 0.9534
Epoch 12/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2071 - binary_accuracy: 0.9552 - val_loss: 0.1873 - val_binary_accuracy: 0.9534
Epoch 13/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2061 - binary_accuracy: 0.9552 - val_loss: 0.1870 - val_binary_accuracy: 0.9534
Epoch 14/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2039 - binary_accuracy: 0.9552 - val_loss: 0.1868 - val_binary_accuracy: 0.9534
Epoch 15/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.2046 - binary_accuracy: 0.9552 - val_loss: 0.1867 - val_binary_accuracy: 0.9534
Epoch 16/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.2038 - binary_accuracy: 0.9552 - val_loss: 0.1866 - val_binary_accuracy: 0.9534
Epoch 17/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2040 - binary_accuracy: 0.9552 - val_loss: 0.1865 - val_binary_accuracy: 0.9534
Epoch 18/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2034 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2034 - binary_accuracy: 0.9552 - val_loss: 0.1864 - val_binary_accuracy: 0.9534
Epoch 19/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2032 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2032 - binary_accuracy: 0.9552 - val_loss: 0.1863 - val_binary_accuracy: 0.9534
Epoch 20/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2016 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.2016 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 21/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2015 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.2015 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 22/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2022 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 23/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2009 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 24/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.2010 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2010 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 25/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.2014 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2014 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 26/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2004 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 27/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2007 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2007 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 28/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2010 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 29/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2004 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 30/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.2006 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 31/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.2000 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.2000 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 32/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1998 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 33/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.2000 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 34/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1993 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1993 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 35/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1995 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1995 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 36/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.1994 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1994 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 37/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1994 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 38/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1986 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 39/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.1986 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.1986 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 40/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1978 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1978 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 41/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1978 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 42/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1980 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 43/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.1984 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.1984 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 44/100

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434/434 [==============================] - 6s 15ms/step - loss: 0.1982 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 45/100

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434/434 [==============================] - 1s 1ms/step - loss: 0.1980 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.1980 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 46/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1967 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 47/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1979 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 48/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 49/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1975 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 50/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1969 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 51/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1958 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 52/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1965 - binary_accuracy: 0.9552 - val_loss: 0.1858 - val_binary_accuracy: 0.9534
Epoch 53/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1970 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 54/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1968 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 55/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 56/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1963 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 57/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1963 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 58/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1967 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 59/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1964 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 60/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1955 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 61/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1965 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 62/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1958 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 63/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1952 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 64/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1948 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 65/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1957 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 66/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1953 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 67/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1953 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 68/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1957 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 69/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1944 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 70/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1949 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 71/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1955 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 72/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1943 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 73/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1948 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 74/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1948 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 75/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1950 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 76/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1946 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 77/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1945 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 78/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1938 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 79/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1944 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 80/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1945 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 81/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1934 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 82/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1938 - binary_accuracy: 0.9552 - val_loss: 0.1859 - val_binary_accuracy: 0.9534
Epoch 83/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1934 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 84/100

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434/434 [==============================] - 2s 4ms/step - loss: 0.1930 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 85/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1929 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 86/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1926 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 87/100

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434/434 [==============================] - 1s 2ms/step - loss: 0.1935 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 88/100

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434/434 [==============================] - 1s 3ms/step - loss: 0.1932 - binary_accuracy: 0.9552 - val_loss: 0.1863 - val_binary_accuracy: 0.9534
Epoch 89/100

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434/434 [==============================] - 2s 5ms/step - loss: 0.1925 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 90/100

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434/434 [==============================] - 2s 3ms/step - loss: 0.1930 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 91/100

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345/434 [======================>.......] - ETA: 0s - loss: 0.1909 - binary_accuracy: 0.9557
364/434 [========================>.....] - ETA: 0s - loss: 0.1905 - binary_accuracy: 0.9558
382/434 [=========================>....] - ETA: 0s - loss: 0.1916 - binary_accuracy: 0.9555
400/434 [==========================>...] - ETA: 0s - loss: 0.1915 - binary_accuracy: 0.9555
419/434 [===========================>..] - ETA: 0s - loss: 0.1920 - binary_accuracy: 0.9553
434/434 [==============================] - 1s 2ms/step - loss: 0.1921 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1921 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 92/100

  1/434 [..............................] - ETA: 0s - loss: 0.2455 - binary_accuracy: 0.9297
 17/434 [>.............................] - ETA: 1s - loss: 0.1730 - binary_accuracy: 0.9600
 33/434 [=>............................] - ETA: 1s - loss: 0.1845 - binary_accuracy: 0.9571
 50/434 [==>...........................] - ETA: 1s - loss: 0.1848 - binary_accuracy: 0.9578
 67/434 [===>..........................] - ETA: 1s - loss: 0.1833 - binary_accuracy: 0.9584
 84/434 [====>.........................] - ETA: 1s - loss: 0.1863 - binary_accuracy: 0.9574
101/434 [=====>........................] - ETA: 1s - loss: 0.1860 - binary_accuracy: 0.9572
118/434 [=======>......................] - ETA: 0s - loss: 0.1856 - binary_accuracy: 0.9574
136/434 [========>.....................] - ETA: 0s - loss: 0.1877 - binary_accuracy: 0.9565
154/434 [=========>....................] - ETA: 0s - loss: 0.1888 - binary_accuracy: 0.9562
172/434 [==========>...................] - ETA: 0s - loss: 0.1893 - binary_accuracy: 0.9562
189/434 [============>.................] - ETA: 0s - loss: 0.1913 - binary_accuracy: 0.9556
206/434 [=============>................] - ETA: 0s - loss: 0.1907 - binary_accuracy: 0.9558
223/434 [==============>...............] - ETA: 0s - loss: 0.1932 - binary_accuracy: 0.9550
240/434 [===============>..............] - ETA: 0s - loss: 0.1929 - binary_accuracy: 0.9551
258/434 [================>.............] - ETA: 0s - loss: 0.1936 - binary_accuracy: 0.9549
275/434 [==================>...........] - ETA: 0s - loss: 0.1927 - binary_accuracy: 0.9553
293/434 [===================>..........] - ETA: 0s - loss: 0.1916 - binary_accuracy: 0.9556
310/434 [====================>.........] - ETA: 0s - loss: 0.1916 - binary_accuracy: 0.9556
328/434 [=====================>........] - ETA: 0s - loss: 0.1911 - binary_accuracy: 0.9558
345/434 [======================>.......] - ETA: 0s - loss: 0.1916 - binary_accuracy: 0.9557
363/434 [========================>.....] - ETA: 0s - loss: 0.1927 - binary_accuracy: 0.9553
381/434 [=========================>....] - ETA: 0s - loss: 0.1926 - binary_accuracy: 0.9553
398/434 [==========================>...] - ETA: 0s - loss: 0.1922 - binary_accuracy: 0.9555
416/434 [===========================>..] - ETA: 0s - loss: 0.1926 - binary_accuracy: 0.9554
434/434 [==============================] - 1s 3ms/step - loss: 0.1928 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1928 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 93/100

  1/434 [..............................] - ETA: 0s - loss: 0.2679 - binary_accuracy: 0.9141
 19/434 [>.............................] - ETA: 1s - loss: 0.2120 - binary_accuracy: 0.9474
 37/434 [=>............................] - ETA: 1s - loss: 0.1942 - binary_accuracy: 0.9540
 55/434 [==>...........................] - ETA: 1s - loss: 0.1954 - binary_accuracy: 0.9543
 73/434 [====>.........................] - ETA: 1s - loss: 0.1934 - binary_accuracy: 0.9547
 90/434 [=====>........................] - ETA: 0s - loss: 0.1882 - binary_accuracy: 0.9562
108/434 [======>.......................] - ETA: 0s - loss: 0.1883 - binary_accuracy: 0.9561
126/434 [=======>......................] - ETA: 0s - loss: 0.1901 - binary_accuracy: 0.9556
144/434 [========>.....................] - ETA: 0s - loss: 0.1915 - binary_accuracy: 0.9552
162/434 [==========>...................] - ETA: 0s - loss: 0.1907 - binary_accuracy: 0.9555
180/434 [===========>..................] - ETA: 0s - loss: 0.1924 - binary_accuracy: 0.9550
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288/434 [==================>...........] - ETA: 0s - loss: 0.1925 - binary_accuracy: 0.9553
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359/434 [=======================>......] - ETA: 0s - loss: 0.1925 - binary_accuracy: 0.9553
376/434 [========================>.....] - ETA: 0s - loss: 0.1929 - binary_accuracy: 0.9553
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410/434 [===========================>..] - ETA: 0s - loss: 0.1932 - binary_accuracy: 0.9553
427/434 [============================>.] - ETA: 0s - loss: 0.1934 - binary_accuracy: 0.9552
434/434 [==============================] - 1s 3ms/step - loss: 0.1933 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1933 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 94/100

  1/434 [..............................] - ETA: 0s - loss: 0.1590 - binary_accuracy: 0.9688
 19/434 [>.............................] - ETA: 1s - loss: 0.1935 - binary_accuracy: 0.9556
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112/434 [======>.......................] - ETA: 0s - loss: 0.1952 - binary_accuracy: 0.9547
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149/434 [=========>....................] - ETA: 0s - loss: 0.1956 - binary_accuracy: 0.9543
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300/434 [===================>..........] - ETA: 0s - loss: 0.1936 - binary_accuracy: 0.9549
319/434 [=====================>........] - ETA: 0s - loss: 0.1942 - binary_accuracy: 0.9549
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366/434 [========================>.....] - ETA: 0s - loss: 0.1932 - binary_accuracy: 0.9551
408/434 [===========================>..] - ETA: 0s - loss: 0.1928 - binary_accuracy: 0.9552
434/434 [==============================] - 1s 2ms/step - loss: 0.1930 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1930 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 95/100

  1/434 [..............................] - ETA: 0s - loss: 0.2320 - binary_accuracy: 0.9453
 37/434 [=>............................] - ETA: 0s - loss: 0.1845 - binary_accuracy: 0.9582
 78/434 [====>.........................] - ETA: 0s - loss: 0.1990 - binary_accuracy: 0.9533
119/434 [=======>......................] - ETA: 0s - loss: 0.1978 - binary_accuracy: 0.9537
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261/434 [=================>............] - ETA: 0s - loss: 0.1938 - binary_accuracy: 0.9548
300/434 [===================>..........] - ETA: 0s - loss: 0.1927 - binary_accuracy: 0.9551
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422/434 [============================>.] - ETA: 0s - loss: 0.1925 - binary_accuracy: 0.9552
434/434 [==============================] - 1s 2ms/step - loss: 0.1922 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 4ms/step - loss: 0.1922 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 96/100

  1/434 [..............................] - ETA: 0s - loss: 0.2378 - binary_accuracy: 0.9453
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131/434 [========>.....................] - ETA: 0s - loss: 0.1956 - binary_accuracy: 0.9540
149/434 [=========>....................] - ETA: 0s - loss: 0.1932 - binary_accuracy: 0.9545
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280/434 [==================>...........] - ETA: 0s - loss: 0.1934 - binary_accuracy: 0.9549
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337/434 [======================>.......] - ETA: 0s - loss: 0.1944 - binary_accuracy: 0.9546
356/434 [=======================>......] - ETA: 0s - loss: 0.1937 - binary_accuracy: 0.9548
375/434 [========================>.....] - ETA: 0s - loss: 0.1938 - binary_accuracy: 0.9548
394/434 [==========================>...] - ETA: 0s - loss: 0.1930 - binary_accuracy: 0.9550
412/434 [===========================>..] - ETA: 0s - loss: 0.1936 - binary_accuracy: 0.9549
431/434 [============================>.] - ETA: 0s - loss: 0.1919 - binary_accuracy: 0.9555
434/434 [==============================] - 1s 3ms/step - loss: 0.1926 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1926 - binary_accuracy: 0.9552 - val_loss: 0.1860 - val_binary_accuracy: 0.9534
Epoch 97/100

  1/434 [..............................] - ETA: 0s - loss: 0.3001 - binary_accuracy: 0.9297
 17/434 [>.............................] - ETA: 1s - loss: 0.2080 - binary_accuracy: 0.9513
 35/434 [=>............................] - ETA: 1s - loss: 0.1938 - binary_accuracy: 0.9558
 53/434 [==>...........................] - ETA: 1s - loss: 0.1869 - binary_accuracy: 0.9575
 71/434 [===>..........................] - ETA: 1s - loss: 0.1861 - binary_accuracy: 0.9580
 89/434 [=====>........................] - ETA: 1s - loss: 0.1839 - binary_accuracy: 0.9584
107/434 [======>.......................] - ETA: 0s - loss: 0.1835 - binary_accuracy: 0.9585
125/434 [=======>......................] - ETA: 0s - loss: 0.1868 - binary_accuracy: 0.9573
143/434 [========>.....................] - ETA: 0s - loss: 0.1883 - binary_accuracy: 0.9568
161/434 [==========>...................] - ETA: 0s - loss: 0.1905 - binary_accuracy: 0.9560
201/434 [============>.................] - ETA: 0s - loss: 0.1907 - binary_accuracy: 0.9560
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285/434 [==================>...........] - ETA: 0s - loss: 0.1921 - binary_accuracy: 0.9554
326/434 [=====================>........] - ETA: 0s - loss: 0.1913 - binary_accuracy: 0.9558
367/434 [========================>.....] - ETA: 0s - loss: 0.1918 - binary_accuracy: 0.9555
404/434 [==========================>...] - ETA: 0s - loss: 0.1920 - binary_accuracy: 0.9554
434/434 [==============================] - 1s 2ms/step - loss: 0.1927 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 3ms/step - loss: 0.1927 - binary_accuracy: 0.9552 - val_loss: 0.1863 - val_binary_accuracy: 0.9534
Epoch 98/100

  1/434 [..............................] - ETA: 0s - loss: 0.1311 - binary_accuracy: 0.9766
 41/434 [=>............................] - ETA: 0s - loss: 0.1739 - binary_accuracy: 0.9609
 81/434 [====>.........................] - ETA: 0s - loss: 0.1840 - binary_accuracy: 0.9577
123/434 [=======>......................] - ETA: 0s - loss: 0.1864 - binary_accuracy: 0.9568
165/434 [==========>...................] - ETA: 0s - loss: 0.1869 - binary_accuracy: 0.9565
207/434 [=============>................] - ETA: 0s - loss: 0.1907 - binary_accuracy: 0.9555
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291/434 [===================>..........] - ETA: 0s - loss: 0.1903 - binary_accuracy: 0.9556
332/434 [=====================>........] - ETA: 0s - loss: 0.1909 - binary_accuracy: 0.9555
373/434 [========================>.....] - ETA: 0s - loss: 0.1900 - binary_accuracy: 0.9557
406/434 [===========================>..] - ETA: 0s - loss: 0.1908 - binary_accuracy: 0.9555
434/434 [==============================] - 1s 1ms/step - loss: 0.1920 - binary_accuracy: 0.9552

434/434 [==============================] - 1s 2ms/step - loss: 0.1920 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534
Epoch 99/100

  1/434 [..............................] - ETA: 0s - loss: 0.1483 - binary_accuracy: 0.9766
 30/434 [=>............................] - ETA: 0s - loss: 0.1850 - binary_accuracy: 0.9586
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100/434 [=====>........................] - ETA: 0s - loss: 0.1955 - binary_accuracy: 0.9542
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257/434 [================>.............] - ETA: 0s - loss: 0.1918 - binary_accuracy: 0.9555
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421/434 [============================>.] - ETA: 0s - loss: 0.1931 - binary_accuracy: 0.9550
434/434 [==============================] - 1s 3ms/step - loss: 0.1924 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1924 - binary_accuracy: 0.9552 - val_loss: 0.1861 - val_binary_accuracy: 0.9534
Epoch 100/100

  1/434 [..............................] - ETA: 0s - loss: 0.2314 - binary_accuracy: 0.9453
 19/434 [>.............................] - ETA: 1s - loss: 0.2090 - binary_accuracy: 0.9502
 37/434 [=>............................] - ETA: 1s - loss: 0.2084 - binary_accuracy: 0.9514
 55/434 [==>...........................] - ETA: 1s - loss: 0.2064 - binary_accuracy: 0.9517
 73/434 [====>.........................] - ETA: 1s - loss: 0.1971 - binary_accuracy: 0.9544
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109/434 [======>.......................] - ETA: 0s - loss: 0.1955 - binary_accuracy: 0.9548
127/434 [=======>......................] - ETA: 0s - loss: 0.1902 - binary_accuracy: 0.9566
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371/434 [========================>.....] - ETA: 0s - loss: 0.1923 - binary_accuracy: 0.9553
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409/434 [===========================>..] - ETA: 0s - loss: 0.1924 - binary_accuracy: 0.9552
427/434 [============================>.] - ETA: 0s - loss: 0.1925 - binary_accuracy: 0.9552
434/434 [==============================] - 1s 3ms/step - loss: 0.1928 - binary_accuracy: 0.9552

434/434 [==============================] - 2s 5ms/step - loss: 0.1928 - binary_accuracy: 0.9552 - val_loss: 0.1862 - val_binary_accuracy: 0.9534

saveRDS(serialize_model(model=nn2,include_optimizer = TRUE),"ori_nn_mod.rds")
#nn2=readRDS("ori_nn_mod.rds")
#nn2=unserialize_model(nn2)



plot(nn2.h)

nn2%>% evaluate(tr.nmat2,nn.tr_lab)

   1/2603 [..............................] - ETA: 0s - loss: 0.1528 - binary_accuracy: 0.9688
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1055/2603 [===========>..................] - ETA: 1s - loss: 0.1752 - binary_accuracy: 0.9554
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1824/2603 [====================>.........] - ETA: 0s - loss: 0.1764 - binary_accuracy: 0.9550
1853/2603 [====================>.........] - ETA: 0s - loss: 0.1765 - binary_accuracy: 0.9550
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1980/2603 [=====================>........] - ETA: 0s - loss: 0.1771 - binary_accuracy: 0.9549
2011/2603 [======================>.......] - ETA: 0s - loss: 0.1770 - binary_accuracy: 0.9550
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2110/2603 [=======================>......] - ETA: 0s - loss: 0.1763 - binary_accuracy: 0.9553
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2179/2603 [========================>.....] - ETA: 0s - loss: 0.1769 - binary_accuracy: 0.9552
2212/2603 [========================>.....] - ETA: 0s - loss: 0.1775 - binary_accuracy: 0.9549
2246/2603 [========================>.....] - ETA: 0s - loss: 0.1782 - binary_accuracy: 0.9548
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2413/2603 [==========================>...] - ETA: 0s - loss: 0.1795 - binary_accuracy: 0.9544
2448/2603 [===========================>..] - ETA: 0s - loss: 0.1796 - binary_accuracy: 0.9544
2482/2603 [===========================>..] - ETA: 0s - loss: 0.1794 - binary_accuracy: 0.9545
2514/2603 [===========================>..] - ETA: 0s - loss: 0.1795 - binary_accuracy: 0.9544
2544/2603 [============================>.] - ETA: 0s - loss: 0.1793 - binary_accuracy: 0.9545
2575/2603 [============================>.] - ETA: 0s - loss: 0.1790 - binary_accuracy: 0.9546
2603/2603 [==============================] - 3s 1ms/step - loss: 0.1791 - binary_accuracy: 0.9546
      0.9546153 

nn2%>% evaluate(val.nmat2,nn.val_lab)

   1/1302 [..............................] - ETA: 0s - loss: 0.2601 - binary_accuracy: 0.9375
  34/1302 [..............................] - ETA: 1s - loss: 0.2219 - binary_accuracy: 0.9559
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 124/1302 [=>............................] - ETA: 1s - loss: 0.2244 - binary_accuracy: 0.9549
 154/1302 [==>...........................] - ETA: 1s - loss: 0.2237 - binary_accuracy: 0.9554
 183/1302 [===>..........................] - ETA: 1s - loss: 0.2198 - binary_accuracy: 0.9575
 212/1302 [===>..........................] - ETA: 1s - loss: 0.2207 - binary_accuracy: 0.9570
 240/1302 [====>.........................] - ETA: 1s - loss: 0.2236 - binary_accuracy: 0.9555
 270/1302 [=====>........................] - ETA: 1s - loss: 0.2246 - binary_accuracy: 0.9549
 299/1302 [=====>........................] - ETA: 1s - loss: 0.2276 - binary_accuracy: 0.9533
 328/1302 [======>.......................] - ETA: 1s - loss: 0.2279 - binary_accuracy: 0.9531
 358/1302 [=======>......................] - ETA: 1s - loss: 0.2275 - binary_accuracy: 0.9534
 388/1302 [=======>......................] - ETA: 1s - loss: 0.2263 - binary_accuracy: 0.9541
 417/1302 [========>.....................] - ETA: 1s - loss: 0.2256 - binary_accuracy: 0.9545
 447/1302 [=========>....................] - ETA: 1s - loss: 0.2268 - binary_accuracy: 0.9538
 477/1302 [=========>....................] - ETA: 1s - loss: 0.2266 - binary_accuracy: 0.9539
 506/1302 [==========>...................] - ETA: 1s - loss: 0.2256 - binary_accuracy: 0.9545
 538/1302 [===========>..................] - ETA: 1s - loss: 0.2253 - binary_accuracy: 0.9546
 571/1302 [============>.................] - ETA: 1s - loss: 0.2246 - binary_accuracy: 0.9550
 602/1302 [============>.................] - ETA: 1s - loss: 0.2249 - binary_accuracy: 0.9548
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 664/1302 [==============>...............] - ETA: 1s - loss: 0.2260 - binary_accuracy: 0.9543
 697/1302 [===============>..............] - ETA: 1s - loss: 0.2250 - binary_accuracy: 0.9549
 730/1302 [===============>..............] - ETA: 0s - loss: 0.2252 - binary_accuracy: 0.9547
 764/1302 [================>.............] - ETA: 0s - loss: 0.2248 - binary_accuracy: 0.9549
 801/1302 [=================>............] - ETA: 0s - loss: 0.2238 - binary_accuracy: 0.9554
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 930/1302 [====================>.........] - ETA: 0s - loss: 0.2246 - binary_accuracy: 0.9550
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1116/1302 [========================>.....] - ETA: 0s - loss: 0.2243 - binary_accuracy: 0.9551
1146/1302 [=========================>....] - ETA: 0s - loss: 0.2247 - binary_accuracy: 0.9550
1175/1302 [==========================>...] - ETA: 0s - loss: 0.2251 - binary_accuracy: 0.9548
1198/1302 [==========================>...] - ETA: 0s - loss: 0.2254 - binary_accuracy: 0.9546
1228/1302 [===========================>..] - ETA: 0s - loss: 0.2255 - binary_accuracy: 0.9545
1255/1302 [===========================>..] - ETA: 0s - loss: 0.2254 - binary_accuracy: 0.9546
1284/1302 [============================>.] - ETA: 0s - loss: 0.2255 - binary_accuracy: 0.9546
1302/1302 [==============================] - 2s 2ms/step - loss: 0.2253 - binary_accuracy: 0.9546
      0.9546142 

nn2.p=nn2%>%predict_proba(val.nmat2)
#summary(nn2.p)
#nn2.cl=ifelse(nn2.p>0.06,1,0)
#table("predicted"=nn2.cl,"actual"=nn.val_lab)


nn2.pred=ROCR::prediction(nn2.p,nn.val_lab)
nn2.perf=ROCR::performance(nn2.pred,"tpr","fpr")
plot(nn2.perf,colorize=T)#,colorize=T
abline(a=0,b=1)

preds <- cbind(p1=log1.p,
               p2=glm1.p[,2],
               p3=glm2.p[,2],
               p4 = lgb1.p,
               p5 =lgb2.p,
               p6=nn1.p,
               p7=nn2.p)

pred.mat <- prediction(preds, labels = matrix(as.factor(val$target), 
                nrow = length(val$target), ncol = 7) )

perf.mat <- performance(pred.mat, "lift","rpp") # for lift lift and rpp
plot(perf.mat,col=as.list(1:7))
abline(a=1,b=0)  # b is slope , a intercept
#abline(a=0,b=1)
legend(x = "bottomright", 
       legend = c("logreg","en1","en2","LGB1","LGB2","nn1","nn2"),
       fill = 1:7)